1 Brief explanation

Every boxplot means a monitoring point (Ponto de monitoramento (or PM) in portuguese). My goal here is to analyze the evolution between decades of each water quality parameter that compounds the Water Quality Index (WQI).

The river flows in the east-west direction as shown in the image below.

The logic behind the sorting in the boxplots is because of 2 main reasons:

  1. The original monitoring point isn’t easy to understand (8 digits, like 87409900)
  2. Changing the original nomenclature to PM1, PM2 (…) makes it easier to understand that the last point has water contributions of every other point upstream.

Some features that I want to add:

  • If the parameter is x, then use x’s classes (with its own classes background color plotted)

  • Define the timescale, should act just like a filter

# plan_wide_19902020 %>%
#   filter(ano_coleta > "1990" &
#          ano_coleta <= "2000")

2 Anotações de coisas por fazer:

  • Descobrir como colocar as estações no sentido correto montante -> jusante nos sumários

87398500, 87398980, 87398900, 87398950, 87405500, 87406900, 87409900

  • Aprender a segmentar o meu dataset por períodos
  • aprender a criar uma nova coluna com a segmentação dos períodos
  • maybe use ~facet.grid
  • aprender a colocar a legenda dentro do gráfico
    • reduzir o tamanho da legenda
  • corrigir os valores 0 de IQA pra NA
  • descobrir como conseguir a equação do lm
  • aprender a pivotar o sumário -> meu sumário do google docs ta batendo direitinho com o do R
  • descobrir se há outros TCCs com disponibilização de códigos
  • Namon tá com com casa decimal "," e ptot tá com "."
  • correlação forte entre condutividade e Namon/Ptot/DBO
1990-2000 2000-2010 2010-2020
1990-2000 2000-2010 2010-2020

3 Instalar os pacotes

# install.packages(tidyverse)

3.1 acessar os pacotes

# library(ggpubr)
pacman::p_load(readr, rmarkdown, readxl, janitor,
               pillar, dplyr, tidyverse,
               # gapminder, 
               knitr, kableExtra, see,
               gridExtra, #modelsummary, 
               gtsummary, ggplot2,
               ggbeeswarm, GGally, ggtext, cowplot,
               report)
# pacman::p_load(tibbletime)
# cite_packages()
knitr::knit_hooks$set(time_it = local({
   now <- NULL
   function(before, options) {
      if (before) {
         # record the current time before each chunk
         now <<- Sys.time()
      } else {
         # calculate the time difference after a chunk
         res <- difftime(Sys.time(), now)
         # return a character string to show the time
         paste("Time for this code chunk to run:", res)
      }
   }
}))

knitr::opts_chunk$set(time_it = TRUE)

3.1.1 referenciando os pacotes

# version$version.string
# citation(package = "tidyverse")

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3.2 importando a planilha

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4 data wrangling

Como há dados faltantes, no cálculo entre o produto das colunas, o R acaba interpretando como se fosse zero, mas na verdade é NA.

plan_wide_19902020 <- plan_wide_19902020 %>% 
   mutate(iqa = ifelse(iqa == 0, NA, iqa))

parametros_IQA <- plan_wide_19902020 %>%
  select(
    codigo,
    ponto_monitoramento,
    pH,
    oxigenio_dissolvido,
    dbo,
    fosforo_total,
    escherichia_coli,
    nitrogenio_amoniacal,
    nitrogenio_kjeldahl,
    nitrogenio_total,
    turbidez,
    temperatura_agua,
    solidos_totais,
    condutividade,
    ano_coleta
  )

write.csv(parametros_IQA,
          "./parametros_IQA.csv",
          row.names = FALSE)

plan_wide_19902020 %>% 
  select(starts_with("iqa_")) %>% 
  mutate(
    teste_iqa_calc = prod() #queria tentar gerar o produtório entre as colunas que já possuem o IQA^2
  )
## # A tibble: 1,179 × 18
##    iqa_dbo iqa_p…¹ iqa_n…² iqa_od iqa_pH iqa_t…³ iqa_c…⁴ iqa_s…⁵ iqa_o…⁶ iqa_t…⁷
##      <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1    54.0    72.7    89.0   84.6   89.8    61.9    76.6    83.9    2.13    1.58
##  2    54.0    78.0    87.6   90.9   87.7    61.9    47.2    84.7    2.15    1.58
##  3    29.0    38.3    59.3   76.2   82.9    80.2    60.4    79.4    2.09    1.58
##  4    69.1    77.4    93.7   89.7   77.0    58.7    63.0    85.8    2.15    1.58
##  5    69.1    71.7    88.8   97.5   89.8    63.1    49.7    83.3    2.18    1.58
##  6    78.1    91.2    94.0   89.9   85.4    65.7    57.1    86.1    2.15    1.58
##  7    61.0    63.4    90.4   79.6   82.9    73.6    47.2    86.1    2.10    1.58
##  8    69.1    67.8    89.4   74.4   85.4    59.7    69.8    73.7    2.08    1.58
##  9    61.0    54.3    83.2   76.6   73.6    65.7    63.0    86.0    2.09    1.58
## 10    78.1    78.0    89.6   86.1   92.0    58.7    56.0    83.7    2.13    1.58
## # … with 1,169 more rows, 8 more variables: iqa_coli_2 <dbl>, iqa_pH_2 <dbl>,
## #   iqa_dbo_2 <dbl>, iqa_nitro_tot_2 <dbl>, iqa_ptot_2 <dbl>, iqa_turb_2 <dbl>,
## #   iqa_sol_tot_2 <dbl>, teste_iqa_calc <dbl>, and abbreviated variable names
## #   ¹​iqa_ptot, ²​iqa_nitro_tot, ³​iqa_turb, ⁴​iqa_coli, ⁵​iqa_sol_tot, ⁶​iqa_od_2,
## #   ⁷​iqa_temp_agua

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5 setting theme

theme_grafs <- function(bg = "white", 
                        coloracao_letra = "black") {
  theme(
    plot.title = 
      element_text(
        hjust = 0.5,
        color = coloracao_letra,
        size = 19),
    
    axis.title.x = 
      # element_text(
      # color = coloracao_letra,
      # size = 15,
      # angle = 0,),
      element_blank(),
    axis.title.y = element_text(
      color = coloracao_letra,
      size = 15,
      angle = 90),
    
    axis.text.x = element_text(
      color = coloracao_letra,
      size = 17),
    axis.text.y = element_text(
      color = coloracao_letra,
      size = 17,
      angle = 0),
    
    strip.background = element_rect(fill = bg,
                                    linetype = 1,
                                    size = 0.5,
                                    color = "black"),
    strip.text = element_text(size = 17),
    panel.background = element_rect(fill = bg),
    plot.background = element_rect(fill = bg),
    plot.margin = margin(l = 5, r = 10,
                         b = 5, t = 5)
  )
}

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6 setting different timescales

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7 setting sumaries

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8 Funções

8.1 criando função para gerar boxplots com percentil 20 e 80

f <- function(x) {
  r <- quantile(x, probs = c(0.05, 0.20, 0.50, 0.80, 0.95))
  names(r) <- c("ymin", "lower", "middle", "upper", "ymax")
  return(r)
}

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8.2 criando função para gerar gráfico de od

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8.3 criando função para gerar gráfico de dbo

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8.4 Ptot

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8.5 E coli

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8.6 Nitrogênio Amoniacal

boxplot_namon <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = nitrogenio_amoniacal, titulo = "Nitrogênio Amoniacal"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   # facet_wrap(~periodo)+
   labs(title = titulo,
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE),
                                 max(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
}

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8.7 Turbidez

boxplot_turb <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = turbidez, titulo = "Turbidez"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
    annotate("rect",
             xmin=-Inf, xmax=Inf,
             ymin=100, ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf, xmax=Inf,
            ymin=40, ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=0, ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   labs(title = titulo,
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.05)),
                      n.breaks = 8,
                      limits = c(
                        # 1,
                        min(plan_wide_19902020$turbidez, na.rm = TRUE),
                        # 500
                        max(plan_wide_19902020$turbidez, na.rm = TRUE)
                      ),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
    theme_grafs()
}

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8.8 pH

8.9 Sólidos Totais

8.10 Condutividade

9 Parâmetros físico-químicos

9.0.1 Oxigênio Dissolvido

Oxigênio Dissolvido no período 1990-2020Time for this code chunk to run: 3.12685894966125

Oxigênio Dissolvido no período 1990-2000Time for this code chunk to run: 0.739910125732422

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ggsave("od.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = od,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p1.png",
       plot = od_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p2.png",
       plot = od_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p3.png",
       plot = od_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

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## # A tibble: 9 × 8
##   par       PM1    PM2   PM3   PM4    PM5   PM6    PM7
##   <chr>   <dbl>  <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>
## 1 max     10.8   10.5  10.3  12.1  19.9   10.2  11.1  
## 2 p95      9.24   9.8   9.16  9.56  8.92   6.45  8.38 
## 3 p80      7.76   8.3   7.52  8.42  6.2    5.5   5.7  
## 4 median   6.4    6.9   5.95  6.3   4.2    2.6   2.9  
## 5 mean     5.99   6.78  5.98  7.01  4.22   2.98  3.60 
## 6 p20      3.84   5.2   4.3   5.72  0.760  0.2   0.8  
## 7 p05      2      4.3   3.14  4.94  0.28   0.1   0.128
## 8 min      0.8    2     2.5   4.2   0.1    0.1   0.1  
## 9 n      101    101    68    30    97     32    65
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500   0.4   3.5   4.9   5.01  6.65  10.9
## 2 87398900   1.9   4     5.5   5.33  6.6   12  
## 3 87398950   1.7   3.2   5.3   5.06  6.18   8.9
## 4 87398980   1.2   3.8   5.6   5.38  6.6    9.2
## 5 87405500   0.2   1.4   2.55  3.28  4     14.2
## 6 87406900   0     1.1   1.9   2.59  3.15  16  
## 7 87409900   0     0.7   2.3   3.12  3.7   10.6
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500  0.38 3.11    4.41  4.57  6.2   12.4
## 2 87398900  3.52 5.25    5.96  6.61  7.3   13.8
## 3 87398950  1.62 3.68    4.92  5.28  6.64  11.9
## 4 87398980  3.37 5.5     6.17  6.48  7.14  13.1
## 5 87405500  0.2  1.3     2.53  2.83  3.66   9.8
## 6 87406900  0.1  0.865   2.4   2.43  3.05   9.1
## 7 87409900  0.1  0.92    2.03  2.43  3.5    8.1

Time for this code chunk to run: 0.259079217910767

9.0.2 Demanda Bioquímica de Oxigênio

(dbo <- ggplot(plan_wide_19902020,
               aes(x = codigo,
                   y = dbo))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=10,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=10,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 1990-2020",
        x="Estação",
        y="mg/L",
        # caption = "Leonardo Fernandes Wink"
        )+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(1,100),
                      trans = "log10")+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 60 rows containing non-finite values (`stat_summary()`).
## Removed 60 rows containing non-finite values (`stat_summary()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 22 rows containing missing values.
## Warning: Removed 30 rows containing missing values.
## Warning: Removed 8 rows containing missing values.

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(sum_dbo_p1 <- plan_wide_19902020 %>%
   select(codigo, dbo, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(dbo, 
           na.rm = TRUE),
     q1 = 
       quantile(dbo, 0.25, 
                na.rm = TRUE),
     median = 
       median(dbo, 
              na.rm = TRUE),
     mean = 
       mean(dbo, 
            na.rm= TRUE),
     q3 = 
       quantile(dbo, 0.75, 
                na.rm = TRUE),
     max = 
       max(dbo, 
           na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500     1     1      2  1.86   2      13
## 2 87398900     1     1      1  1.52   2       6
## 3 87398950     1     1      1  1.66   2       6
## 4 87398980     1     1      1  1.13   1       2
## 5 87405500     1     2      3  5.37   5      64
## 6 87406900     1     4      5  9     11      26
## 7 87409900     2     3      4  6.97   9.5    31
(sum_dbo_p2 <- plan_wide_19902020 %>%
    select(codigo, dbo, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(dbo, 
            na.rm = TRUE),
      q1 = 
        quantile(dbo, 0.25, 
                 na.rm = TRUE),
      median = 
        median(dbo, 
               na.rm = TRUE),
      mean = 
        mean(dbo, 
             na.rm= TRUE),
      q3 = 
        quantile(dbo, 0.75, 
                 na.rm = TRUE),
      max = 
        max(dbo, 
            na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500     1     1      1  1.58   2       5
## 2 87398900     1     1      1  1.40   2       5
## 3 87398950     1     1      1  1.66   2       5
## 4 87398980     1     1      1  1.30   1       5
## 5 87405500     1     2      4  4.67   6.5    14
## 6 87406900     1     3      5  6.53   8      28
## 7 87409900     1     3      6  6.31   9      15
(sum_dbo_p3 <- plan_wide_19902020 %>%
    select(codigo, dbo, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(dbo, 
            na.rm = TRUE),
      q1 = 
        quantile(dbo, 0.25, 
                 na.rm = TRUE),
      median = 
        median(dbo, 
               na.rm = TRUE),
      mean = 
        mean(dbo, 
             na.rm= TRUE),
      q3 = 
        quantile(dbo, 0.75, 
                 na.rm = TRUE),
      max = 
        max(dbo, 
            na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500     1     1    1.5  2.15  3        7
## 2 87398900     1     1    1    1.51  2        5
## 3 87398950     1     1    2    2.65  2       18
## 4 87398980     1     1    1    1.32  2        2
## 5 87405500     1     3    4    5.28  6.25    21
## 6 87406900     1     3    5    6.58 10       24
## 7 87409900     1     3    4.5  6.18  8       18

Time for this code chunk to run: 0.253508806228638

ggsave("dbo.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = dbo,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p1.png",
       plot = dbo_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p2.png",
       plot = dbo_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p3.png",
       plot = dbo_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

Time for this code chunk to run: 4.29959893226624

9.0.3 Fósforo total

(ptot <- ggplot(plan_wide_19902020,
                aes(codigo,
                    fosforo_total))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
  stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
  facet_wrap(~periodo)+
    labs(title = "Fósforo total no período 1990-2020",
         x="Estação",
         y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                 max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " ")
                      )+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 134 rows containing non-finite values (`stat_summary()`).
## Removed 134 rows containing non-finite values (`stat_summary()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 47 rows containing missing values.
## Warning: Removed 31 rows containing missing values.
## Warning: Removed 56 rows containing missing values.

Fósforo total no período 1990-2020Time for this code chunk to run: 1.57841110229492

(ptot_p1<-ggplot(plan_wide_19902020%>% 
                   filter(ano_coleta>"1990" &
                             ano_coleta<="2000"),
                 aes(codigo,
                     fosforo_total))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.15,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.1,
             ymax=0.15,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=0.1,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Fósforo total no período 1990-2000",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                  max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                       trans = "log10")+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

Time for this code chunk to run: 0.740449905395508

(ptot_p2 <- ggplot(plan_wide_19902020%>% 
                      filter(ano_coleta>"2000" &
                                ano_coleta<="2010"),
                   aes(codigo,
                       fosforo_total))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.15,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.1,
             ymax=0.15,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=0.1,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Fósforo total no período 2000-2010",
         x="Estação",
         y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                 max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                      trans = "log10")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

Time for this code chunk to run: 0.779342889785767

(ptot_p3 <- ggplot(plan_wide_19902020%>% 
                      filter(ano_coleta>"2010" &
                                ano_coleta<="2020"),
                   aes(codigo,
                       fosforo_total))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.15,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.1,
             ymax=0.15,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=0.1,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Fósforo total no período 2010-2020",
         x="Estação",
         y="mg/L")+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                  max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                       trans = "log10")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

Time for this code chunk to run: 0.707337856292725

(sum_ptot_p1 <- plan_wide_19902020 %>%
    select(codigo, fosforo_total, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(fosforo_total, na.rm = TRUE),
     q1 = 
       quantile(fosforo_total, 0.25, na.rm = TRUE),
     median = 
       median(fosforo_total, na.rm = TRUE),
     mean = 
       mean(fosforo_total, na.rm= TRUE),
     q3 = 
       quantile(fosforo_total, 0.75, na.rm = TRUE),
     max = 
       max(fosforo_total, na.rm = TRUE)))
## # A tibble: 7 × 7
##   codigo      min     q1 median   mean     q3   max
##   <chr>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <dbl>
## 1 87398500 0.0097 0.0593 0.0881 0.123  0.14   0.863
## 2 87398900 0.0023 0.0468 0.0678 0.0747 0.0883 0.247
## 3 87398950 0.0202 0.0544 0.0737 0.0751 0.0904 0.179
## 4 87398980 0.01   0.0254 0.0547 0.0708 0.114  0.189
## 5 87405500 0.017  0.171  0.281  0.417  0.492  2.32 
## 6 87406900 0.156  0.270  0.508  0.785  1.07   2.79 
## 7 87409900 0.107  0.258  0.384  0.489  0.712  1.53
(sum_ptot_p2 <- plan_wide_19902020 %>%
    select(codigo, fosforo_total, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(fosforo_total, na.rm = TRUE),
      q1 = 
        quantile(fosforo_total, 0.25, na.rm = TRUE),
      median = 
        median(fosforo_total, na.rm = TRUE),
      mean = 
        mean(fosforo_total, na.rm= TRUE),
      q3 = 
        quantile(fosforo_total, 0.75, na.rm = TRUE),
      max = 
        max(fosforo_total, na.rm = TRUE)))
## # A tibble: 7 × 7
##   codigo      min     q1 median  mean    q3   max
##   <chr>     <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.025  0.094   0.131 0.148 0.16  0.637
## 2 87398900 0.015  0.0764  0.104 0.140 0.164 0.646
## 3 87398950 0.036  0.116   0.171 0.180 0.207 0.485
## 4 87398980 0.0115 0.052   0.076 0.101 0.103 1    
## 5 87405500 0.046  0.261   0.406 0.547 0.681 1.98 
## 6 87406900 0.056  0.338   0.599 0.752 0.967 3.49 
## 7 87409900 0.043  0.325   0.624 0.677 0.989 1.57
(sum_ptot_p3 <- plan_wide_19902020 %>%
    select(codigo, fosforo_total, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(fosforo_total, na.rm = TRUE),
      q1 = 
        quantile(fosforo_total, 0.25, na.rm = TRUE),
      median = 
        median(fosforo_total, na.rm = TRUE),
      mean = 
        mean(fosforo_total, na.rm= TRUE),
      q3 = 
        quantile(fosforo_total, 0.75, na.rm = TRUE),
      max = 
        max(fosforo_total, na.rm = TRUE)))
## # A tibble: 7 × 7
##   codigo     min     q1 median  mean    q3   max
##   <chr>    <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.061 0.118   0.163 0.166 0.186 0.381
## 2 87398900 0.057 0.0935  0.130 0.163 0.168 0.444
## 3 87398950 0.07  0.132   0.156 0.292 0.221 3.11 
## 4 87398980 0.019 0.0625  0.106 0.144 0.170 0.59 
## 5 87405500 0.013 0.187   0.332 0.361 0.45  0.803
## 6 87406900 0.089 0.254   0.364 0.448 0.560 1.26 
## 7 87409900 0.203 0.259   0.369 0.488 0.564 1.7

Time for this code chunk to run: 0.254715919494629

ggsave("ptot.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = ptot,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p1.png",
       plot = ptot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p2.png",
       plot = ptot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p3.png",
       plot = ptot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

Time for this code chunk to run: 4.63362312316895

9.0.4 Escherichia coli

(ecoli <- boxplot_ecoli(
  titulo = "*Escherichia coli* no período 1990-2020"
)+
  facet_wrap(~periodo)
)

Escherichia-coli-gravataí no período 1990-2020

(ecoli <- ggplot(plan_wide_19902020,
                 aes(codigo,
                     escherichia_coli))+
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=3200, ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=800, ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=160, ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=0, ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "*Escherichia coli* no período 1990-2020",
        x="Estação",
        y="NMP/100mL")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      # n.breaks = 9,
                      n.breaks = 6,
                      limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                 max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()+
    theme(
        axis.text.y = element_text(
          angle = 90, 
          # size=15,
          # face=2
        ),
        plot.title = 
          element_markdown(
            hjust = 0.5,
            color = "black",
            size = 19)
    )
)

Escherichia-coli-gravataí no período 1990-2020Time for this code chunk to run: 3.55012106895447

(ecoli_p1 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"1990" &
                                 ano_coleta<="2000"),
                    aes(codigo,
                        escherichia_coli))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3200,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=800,
             ymax=3200,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=160,
             ymax=800,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=160,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Escherichia coli no período 1990-2000",
         x="Estação",
         y="NMP/100mL")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                 max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

Time for this code chunk to run: 0.889932870864868

(ecoli_p2 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"2000" &
                                 ano_coleta<="2010"),
                    aes(codigo,
                        escherichia_coli))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3200,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=800,
             ymax=3200,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=160,
             ymax=800,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=160,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Escherichia coli no período 2000-2010",
         x="Estação",
         y="NMP/100mL")+
    scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                       n.breaks = 9,
                       limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                  max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                       trans = "log10",
                       labels = scales::number_format(accuracy = 1,
                                                      decimal.mark = ",",
                                                      big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

Time for this code chunk to run: 0.830866813659668

(ecoli_p3 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"2010" &
                                 ano_coleta<="2020"),
                    aes(codigo,
                        escherichia_coli))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3200,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=800,
             ymax=3200,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=160,
             ymax=800,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=160,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Escherichia coli no período 2010-2020",
         x="Estação",
         y="NMP/100mL")+
    scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                       n.breaks = 9,
                       limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                  max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                       trans = "log10",
                       labels = scales::number_format(accuracy = 1,
                                                      decimal.mark = ",",
                                                      big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

Time for this code chunk to run: 1.1879780292511

(sum_ecoli_p1 <- plan_wide_19902020 %>%
    select(codigo, escherichia_coli, ano_coleta) %>% 
    filter(ano_coleta>"1990" &
              ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(escherichia_coli, 
           na.rm = TRUE),
     q1 = 
       quantile(escherichia_coli, 0.25, 
                na.rm = TRUE),
     median = 
       median(escherichia_coli, 
              na.rm = TRUE),
     mean = 
       mean(escherichia_coli, 
            na.rm= TRUE),
     q3 = 
       quantile(escherichia_coli, 0.75, 
                na.rm = TRUE),
     max = 
       max(escherichia_coli, 
           na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median   mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl>
## 1 87398500  32   136     240   854.    720 19200
## 2 87398900  16    68     160   548.    480  7760
## 3 87398950   2.4  12.8   268  4039.  10000 28000
## 4 87398980   4   160     243. 2907.    446 25600
## 5 87405500   1.6  12.8    24   545.    128 18400
## 6 87406900  13.6  61.6   192   718.    414 12800
## 7 87409900   2.4  12.8    64    97.7   128   720
(sum_ecoli_p2 <- plan_wide_19902020 %>%
    select(codigo, escherichia_coli, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(escherichia_coli, 
            na.rm = TRUE),
      q1 = 
        quantile(escherichia_coli, 0.25, 
                 na.rm = TRUE),
      median = 
        median(escherichia_coli, 
               na.rm = TRUE),
      mean = 
        mean(escherichia_coli, 
             na.rm= TRUE),
      q3 = 
        quantile(escherichia_coli, 0.75, 
                 na.rm = TRUE),
      max = 
        max(escherichia_coli, 
            na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median   mean     q3    max
##   <chr>    <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
## 1 87398500  21.6   91    150   1335.   308   27200
## 2 87398900  11     70    133.   444.   414.   2600
## 3 87398950  20    400    720    935.  1120    5500
## 4 87398980  24    110.   195    410.   289.   8800
## 5 87405500   4.7  162   2400  25445. 12950  490000
## 6 87406900   8    172  12800  66370. 62300  650000
## 7 87409900  16   7355. 35500  72440. 68750  460000
(sum_ecoli_p3 <- plan_wide_19902020 %>%
    select(codigo, escherichia_coli, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(escherichia_coli, 
            na.rm = TRUE),
      q1 = 
        quantile(escherichia_coli, 0.25, 
                 na.rm = TRUE),
      median = 
        median(escherichia_coli, 
               na.rm = TRUE),
      mean = 
        mean(escherichia_coli, 
             na.rm= TRUE),
      q3 = 
        quantile(escherichia_coli, 0.75, 
                 na.rm = TRUE),
      max = 
        max(escherichia_coli, 
            na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo      min      q1 median    mean      q3      max
##   <chr>     <dbl>   <dbl>  <dbl>   <dbl>   <dbl>    <dbl>
## 1 87398500   90     155.    260     409.    451     2420 
## 2 87398900   10      52.8   107     245.    313     1553.
## 3 87398950  108.    250     487    1424.   1553.   10462 
## 4 87398980   40.8   140.    242.    529.    738.    2400 
## 5 87405500  632    8965   19232. 109992.  70750  1400000 
## 6 87406900 1440   23100   34500  230828. 140500  3400000 
## 7 87409900 2000   20100   38400   83128.  83680   345000

Time for this code chunk to run: 0.24810004234314

ggsave("ecoli.png",
       plot = ecoli,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p1.png",
       plot = ecoli_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p2.png",
       plot = ecoli_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p3.png",
       plot = ecoli_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

Time for this code chunk to run: 4.2718288898468

9.0.5 Nitrogênio amoniacal

(namon <- plan_wide_19902020 %>% 
  boxplot_namon(
    eixo_y = nitrogenio_amoniacal,
    titulo = "Nitrogênio Amoaniacal no período 1990-2020"
    )+
  facet_wrap(~periodo)
 )

nitrogenio-gravataí no período 1990-2020Time for this code chunk to run: 1.68492484092712

periodo_inicial <- as.Date("1990-01-01", "%Y-%m-%d")
periodo_final <- as.Date("2021-01-01",  "%Y-%m-%d")

(nitro_line <- 
  plan_wide_19902020 %>%
  filter(ano_coleta > "1990" &
           ano_coleta <= "2020") %>%
  dplyr::select(codigo, nitrogenio_amoniacal, data_coleta, periodo) %>%
  # group_by(codigo) %>%
  mutate(
    ponto_monitoramento = case_when(
      codigo == "87398500" ~ "PM1",
      codigo == "87398980" ~ "PM2",
      codigo == "87398900" ~ "PM3",
      codigo == "87398950" ~ "PM4",
      codigo == "87405500" ~ "PM5",
      codigo == "87406900" ~ "PM6",
      codigo == "87409900" ~ "PM7"
    )
  ) %>% 
    # pivot_wider(
    #   names_from = codigo,
    #   values_from = nitro_amon,
    #   id_cols = data_coleta
    # ) %>% 
    ggplot(
      aes(x = data_coleta,
          y = nitrogenio_amoniacal,
          # color = codigo
      ))+
    # geom_rect(
    #   aes(xmin = periodo_inicial, 
    #       xmax = periodo_final,
    #       ymin = 13.3, 
    #       ymax = Inf,
    #       alpha= 0.005,
    #       fill= "#ac5079"),
    # show.legend = FALSE)+ #>pior classe
    # geom_rect(
    #   aes(xmin = periodo_inicial, 
    #       xmax = periodo_final,
  #       ymin= 3.7,
  #       ymax= 13.3,
  #       alpha= 0.005,
  #       fill= "#fcf7ab"),
  #    show.legend = FALSE)+ #classe 3
  # geom_rect(
  #   aes(xmin = periodo_inicial, 
  #       xmax = periodo_final,
  #       ymin= 0,
  #       ymax= 3.7,
  #       alpha= 0.005,
  #       fill= "blue"
  #         # "#8dcdeb"
  #         ),
  #    show.legend = FALSE)+ #classe 1
  annotate("rect",
           xmin= periodo_inicial,
           xmax= periodo_final,
           ymin=13.3,
           ymax=Inf,
           alpha= 0.7,
           fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin= periodo_inicial,
             xmax= periodo_final,
             ymin=3.7,
             ymax=13.3,
             alpha= 0.7,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin= periodo_inicial,
             xmax= periodo_final,
             ymin= -Inf,
             ymax=3.7,
             alpha= 0.7,
             fill="#8dcdeb")+ #classe 1
    geom_line(
      # aes(color = codigo),
      na.rm = TRUE)+
    geom_point(
      # aes(color = codigo),
      na.rm = TRUE)+
    scale_x_date(
      limits = as.Date(c(
        "1990-01-01", 
        "2021-01-01"
        # NA #pode usar NA também
      )),
      expand = c(0.0, 0.0),
      date_breaks = "10 years",
      minor_breaks = "5 years",
      date_labels = "%Y",
    )+
    # geom_smooth(
    #   # aes(color = codigo),
    #   method = "lm",
    #   # formula = y ~ poly(x, 2),
    #   # span = 0.2,
    #   se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
    #   aes(group = 1),
    #   alpha =.5,
    #   na.rm = TRUE,
    #   size = 0.3,
    #   # fullrange = TRUE,
  #   # show.legend = TRUE
  # )+
  # stat_smooth(
  #   geom = "smooth",
  #   # span = 0.2,
  #   se = FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
  #   # aes(group = 1),
  #   # alpha =.5,
  #   na.rm = TRUE,
  #   # size = 0.3,
  #   fullrange = TRUE,
  #   show.legend = TRUE
  # )+
  facet_wrap(
    ~ponto_monitoramento,
    nrow = 4,
  )+
    theme_bw()
)

Time for this code chunk to run: 1.26403880119324

(namon_p1 <- ggplot(plan_wide_19902020 %>% 
                      filter(ano_coleta>"1990" &
                               ano_coleta<="2000"),
                    aes(codigo,
                        nitrogenio_amoniacal))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3.7,
             ymax=13.3,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=3.7,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 1990-2000",
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE),
                                 max(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.758342981338501

(namon_p2 <- ggplot(plan_wide_19902020 %>% 
                      filter(ano_coleta>"2000" &
                               ano_coleta<="2010"),
                    aes(codigo,
                        nitrogenio_amoniacal))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 2000-2010",
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE),
                                 max(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.794203996658325

(namon_p3 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"2010" &
                                 ano_coleta<="2020"),
                    aes(codigo,
                        nitrogenio_amoniacal))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 2010-2020",
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE),
                                 max(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.768031120300293

grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3)

Time for this code chunk to run: 2.33321499824524

(sum_namon_p1 <- plan_wide_19902020 %>%
   select(codigo, nitrogenio_amoniacal, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(nitrogenio_amoniacal, 
           na.rm = TRUE),
     q1 = 
       quantile(nitrogenio_amoniacal, 0.25, 
                na.rm = TRUE),
     median = 
       median(nitrogenio_amoniacal, 
              na.rm = TRUE),
     mean = 
       mean(nitrogenio_amoniacal, 
            na.rm= TRUE),
     q3 = 
       quantile(nitrogenio_amoniacal, 0.75, 
                na.rm = TRUE),
     max = 
       max(nitrogenio_amoniacal, 
           na.rm = TRUE),
      n = 
       length(nitrogenio_amoniacal)
   )
)
## # A tibble: 7 × 8
##   codigo       min     q1 median    mean     q3     max     n
##   <chr>      <dbl>  <dbl>  <dbl>   <dbl>  <dbl>   <dbl> <int>
## 1 87398500   0.08   0.165  0.205   0.246  0.28     0.91   101
## 2 87398900   0.087  0.17   0.2     0.248  0.242    1.13   101
## 3 87398950 Inf     NA     NA     NaN     NA     -Inf       68
## 4 87398980   0.027  0.12   0.15    0.158  0.188    0.3     30
## 5 87405500   0.43   0.875  1.74    4.28   5.88    17.7     97
## 6 87406900   0.51   1.14   2.78    5.35   7.50    26       32
## 7 87409900 Inf     NA     NA     NaN     NA     -Inf       65
(sum_namon_p2 <- plan_wide_19902020 %>%
    select(codigo, nitrogenio_amoniacal, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(nitrogenio_amoniacal, 
            na.rm = TRUE),
      q1 = 
        quantile(nitrogenio_amoniacal, 0.25, 
                 na.rm = TRUE),
      median = 
        median(nitrogenio_amoniacal, 
               na.rm = TRUE),
      mean = 
        mean(nitrogenio_amoniacal, 
             na.rm= TRUE),
      q3 = 
        quantile(nitrogenio_amoniacal, 0.75, 
                 na.rm = TRUE),
      max = 
        max(nitrogenio_amoniacal, 
            na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3    max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl>  <dbl>
## 1 87398500 0.031 0.16   0.21  0.259 0.29   1.22 
## 2 87398900 0.03  0.16   0.21  0.284 0.342  2.77 
## 3 87398950 0.044 0.214  0.276 0.268 0.349  0.413
## 4 87398980 0.024 0.098  0.14  0.161 0.21   0.551
## 5 87405500 0.038 0.79   2.09  3.49  4.54  18    
## 6 87406900 0.03  1.05   2.66  4.27  5.62  19.4  
## 7 87409900 0.453 0.78   2.46  3.84  4.77  16.6
(sum_namon_p3 <- plan_wide_19902020 %>%
    select(codigo, nitrogenio_amoniacal, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(nitrogenio_amoniacal, 
            na.rm = TRUE),
      q1 = 
        quantile(nitrogenio_amoniacal, 0.25, 
                 na.rm = TRUE),
      median = 
        median(nitrogenio_amoniacal, 
               na.rm = TRUE),
      mean = 
        mean(nitrogenio_amoniacal, 
             na.rm= TRUE),
      q3 = 
        quantile(nitrogenio_amoniacal, 0.75, 
                 na.rm = TRUE),
      max = 
        max(nitrogenio_amoniacal, 
            na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3    max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl>  <dbl>
## 1 87398500  0.03 0.085  0.1   0.150 0.198  0.419
## 2 87398900  0.03 0.1    0.192 0.213 0.248  0.699
## 3 87398950  0.02 0.165  0.263 0.315 0.4    0.951
## 4 87398980  0.02 0.06   0.1   0.179 0.235  0.717
## 5 87405500  0.05 0.808  1.60  2.55  3.78   9.12 
## 6 87406900  0.03 1.09   2.16  3.61  5.02  21.2  
## 7 87409900  0.04 1.44   2.5   3.40  4.39  18.8

Time for this code chunk to run: 0.327318906784058

ggsave("namon.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = namon,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p1.png",
       plot = namon_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p2.png",
       plot = namon_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p3.png",
       plot = namon_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")

Time for this code chunk to run: 7.71082401275635

9.0.6 Turbidez

(turb <- ggplot(plan_wide_19902020,
                   aes(codigo,
                       turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "Turbidez no período 1990-2020",
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.05)),
                      n.breaks = 8,
                      limits = c(
                        # 1,
                        min(plan_wide_19902020$turbidez, na.rm = TRUE),
                        # 500
                        max(plan_wide_19902020$turbidez, na.rm = TRUE)
                      ),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

turbidez-gravataí no período 1990-2020Time for this code chunk to run: 1.50802898406982

(turb_line <- plan_wide_19902020 %>%
  filter(ano_coleta > "1990" &
           ano_coleta <= "2020") %>%
  select(codigo, turbidez, data_coleta, periodo) %>%
  group_by(codigo) %>%
  ggplot(
    aes(x = data_coleta,
        y = turbidez,
        color = codigo
    ))+
    geom_line(
      # aes(color = codigo),
      na.rm = TRUE)+
    geom_point(
      # aes(color = codigo),
      na.rm = TRUE)+
    scale_x_date(
      limits = as.Date(c(
        "1990-01-01", 
        "2021-01-01"
        # NA #pode usar NA também
      )),
      expand = c(0.0, 0.0),
      date_breaks = "10 years",
      minor_breaks = "5 years",
      date_labels = "%Y",
    )+
  # geom_smooth(
  #   # aes(color = codigo),
  #   method = "lm",
  #   # formula = y ~ poly(x, 2),
  #   # span = 0.2,
  #   se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
  #   aes(group = 1),
  #   alpha =.5,
  #   na.rm = TRUE,
  #   size = 0.3,
  #   # fullrange = TRUE,
  #   # show.legend = TRUE
  # )+
  # stat_smooth(
  #   geom = "smooth",
  #   # span = 0.2,
  #   se = FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
  #   # aes(group = 1),
  #   # alpha =.5,
  #   na.rm = TRUE,
  #   # size = 0.3,
  #   fullrange = TRUE,
  #   show.legend = TRUE
  # )+
  facet_wrap(
    ~codigo,
    nrow = 4,
  )+
  theme_bw()
)

Time for this code chunk to run: 1.31414103507996

(turb_p1 <- ggplot(plan_wide_19902020 %>% 
                     filter(ano_coleta>"1990" &
                              ano_coleta<="2000"),
                   aes(codigo,
                       turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 1990-2000",
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.923189163208008

(turb_p2 <- ggplot(plan_wide_19902020 %>% 
                     filter(ano_coleta>"2000" &
                              ano_coleta<="2010"),
                   aes(codigo,
                       turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 2000-2010",
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.764994859695435

(turb_p3 <- ggplot(plan_wide_19902020 %>% 
                     filter(ano_coleta>"2010" &
                              ano_coleta<="2020"),
                   aes(codigo,
                       turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 2010-2020",
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.734085083007812

grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3)

Time for this code chunk to run: 2.32150292396545

(sum_turb_p1 <- plan_wide_19902020 %>%
   select(codigo, turbidez, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(turbidez, 
           na.rm = TRUE),
     q1 = 
       quantile(turbidez, 0.25, 
                na.rm = TRUE),
     median = 
       median(turbidez, 
              na.rm = TRUE),
     mean = 
       mean(turbidez, 
            na.rm= TRUE),
     q3 = 
       quantile(turbidez, 0.75, 
                na.rm = TRUE),
     max = 
       max(turbidez, 
           na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500   6.2  19     34.5  63.5  67     461
## 2 87398900   9    19     49.5  61.5  73.8   460
## 3 87398950   9.6  16     22    33.3  48.8   144
## 4 87398980  16    32.8   43    66.8  90.5   190
## 5 87405500   8.5  23.5   47    47.5  58     159
## 6 87406900  33    54.8   67    77.7  81.5   199
## 7 87409900   5.8  15     25    32.2  48      76
(sum_turb_p2 <- plan_wide_19902020 %>%
    select(codigo, turbidez, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(turbidez, 
               na.rm = TRUE),
      mean = 
        mean(turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(turbidez, 
            na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500     9  41.2   55.5  71.1  74.2   428
## 2 87398900    39  57     78   107.  116.    475
## 3 87398950    39  47     64    96.5  90     330
## 4 87398980    24  37     50    64.5  87     176
## 5 87405500    32  46     63.5  70.3  76     341
## 6 87406900    35  49     62    69.9  75.5   284
## 7 87409900    40  45     60    70.4  90     151
(sum_turb_p3 <- plan_wide_19902020 %>%
    select(codigo, turbidez, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(turbidez, 
               na.rm = TRUE),
      mean = 
        mean(turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(turbidez, 
            na.rm = TRUE))
) 
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500  8.52  16.4   29    33.3  43     85 
## 2 87398900 14.8   39.2   48.3  66.7  73.4  299 
## 3 87398950 16     29.9   41    51.6  65    230 
## 4 87398980 11     19.4   33.6  39.5  42.2  110.
## 5 87405500 10.0   29.0   41    42.9  54.5  131 
## 6 87406900  9.62  23     39    41.2  52    122 
## 7 87409900  9.68  22.0   34.0  40.5  47    182.

Time for this code chunk to run: 0.360772132873535

ggsave("turb.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = turb,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p1.png",
       plot = turb_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p2.png",
       plot = turb_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p3.png",
       plot = turb_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")

Time for this code chunk to run: 8.09145998954773

9.0.7 pH

(pH <- ggplot(plan_wide_19902020,
                 aes(codigo,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "pH no período 1990-2020",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

pH-gravataí no período 1990-2020Time for this code chunk to run: 2.29897689819336

(pH_p1 <- ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta>"1990" &
                            ano_coleta<="2000"),
                 aes(codigo,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 1990-2000",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.825370073318481

(pH_p2 <- ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta>"2000" &
                            ano_coleta<="2010"),
                 aes(codigo,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 2000-2010",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.700754880905151

(pH_p3 <- ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta>"2010" &
                            ano_coleta<="2020"),
                 aes(codigo,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 2010-2020",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.680684089660645

grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3)

Time for this code chunk to run: 2.04337596893311

(sum_pH_p1 <- plan_wide_19902020 %>%
   select(codigo, pH, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(pH, 
           na.rm = TRUE),
     q1 = 
       quantile(pH, 0.25, 
                na.rm = TRUE),
     median = 
       median(pH, 
              na.rm = TRUE),
     mean = 
       mean(pH, 
            na.rm= TRUE),
     q3 = 
       quantile(pH, 0.75, 
                na.rm = TRUE),
     max = 
       max(pH, 
           na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500   5    6.18   6.59  6.51  6.82   7.9
## 2 87398900   5.2  6      6.3   6.33  6.63   7.9
## 3 87398950   5.4  6.29   6.4   6.49  6.72   8.1
## 4 87398980   5.3  5.93   6.2   6.16  6.3    7.3
## 5 87405500   5    6.3    6.4   6.47  6.7    9.3
## 6 87406900   5.5  6.18   6.45  6.43  6.8    7.3
## 7 87409900   4.5  6.2    6.4   6.44  6.7    7.4
(sum_pH_p2 <- plan_wide_19902020 %>%
    select(codigo, pH, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
) 
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500   5.3   6.3   6.6   6.59  6.88   7.9
## 2 87398900   5.5   6.4   6.65  6.63  6.9    7.5
## 3 87398950   6     6.6   6.8   6.89  7.25   7.6
## 4 87398980   5.8   6.3   6.5   6.63  7      7.5
## 5 87405500   5.2   6.4   6.6   6.68  6.9    8.3
## 6 87406900   5.5   6.4   6.7   6.66  6.9    8.6
## 7 87409900   5.8   6.5   6.8   6.77  7      8.4
(sum_pH_p3 <- plan_wide_19902020 %>%
    select(codigo, pH, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500  5.47  6.28   6.42  6.47  6.60  7.3 
## 2 87398900  5.68  6.36   6.5   6.57  6.84  7.4 
## 3 87398950  5.71  6.28   6.46  6.46  6.68  7   
## 4 87398980  5.42  6.10   6.36  6.39  6.6   7.2 
## 5 87405500  5.64  6.34   6.5   6.49  6.7   7.01
## 6 87406900  5.6   6.4    6.48  6.51  6.77  7.3 
## 7 87409900  5.59  6.46   6.6   6.57  6.76  7.2

Time for this code chunk to run: 0.251217126846313

ggsave("pH.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = pH,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p1.png",
       plot = pH_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p2.png",
       plot = pH_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p3.png",
       plot = pH_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")

Time for this code chunk to run: 7.51497602462769

9.0.8 Sólidos totais

(SolTot <- ggplot(plan_wide_19902020,
                  aes(codigo,
                      solidos_totais))+
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "Sólidos totais no período 1990-2020",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

sólidos-totais-gravataí no período 1990-2020Time for this code chunk to run: 1.84275102615356

(SolTot_p1 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"1990" &
                                ano_coleta<="2000"),
                     aes(codigo,
                         solidos_totais))+
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 1990-2000",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.935385227203369

(SolTot_p2 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"2000" &
                                ano_coleta<="2010"),
                     aes(codigo,
                         solidos_totais))+
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 2000-2010",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

Time for this code chunk to run: 0.963917016983032

(SolTot_p3 <- ggplot(plan_wide_19902020 %>% 
                        filter(ano_coleta>"2010" &
                                  ano_coleta<="2020"),
                     aes(codigo,
                         solidos_totais))+
    annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=500,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Sólidos totais no período 2010-2020",
         x="Estação",
         y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.909337997436523

grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3)

Time for this code chunk to run: 2.60734796524048

(sum_SolTot_p1 <- plan_wide_19902020 %>%
   select(codigo, solidos_totais, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(solidos_totais, 
           na.rm = TRUE),
     q1 = 
       quantile(solidos_totais, 0.25, 
                na.rm = TRUE),
     median = 
       median(solidos_totais, 
              na.rm = TRUE),
     mean = 
       mean(solidos_totais, 
            na.rm= TRUE),
     q3 = 
       quantile(solidos_totais, 0.75, 
                na.rm = TRUE),
     max = 
       max(solidos_totais, 
           na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500    46  84.5   95   122.   120    510
## 2 87398900    18  74.5   97   111.   122.   474
## 3 87398950    10  76.5   91    90.9  106.   155
## 4 87398980    48  63.5   81.5 104.   126.   337
## 5 87405500    70 101    121   133.   151    361
## 6 87406900    89 118    155   165.   210    279
## 7 87409900    20  99.5  122   128.   143    381
(sum_SolTot_p2 <- plan_wide_19902020 %>%
    select(codigo, solidos_totais, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(solidos_totais, 
            na.rm = TRUE),
      q1 = 
        quantile(solidos_totais, 0.25, 
                 na.rm = TRUE),
      median = 
        median(solidos_totais, 
               na.rm = TRUE),
      mean = 
        mean(solidos_totais, 
             na.rm= TRUE),
      q3 = 
        quantile(solidos_totais, 0.75, 
                 na.rm = TRUE),
      max = 
        max(solidos_totais, 
            na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500    28  80     100  111.   123.   412
## 2 87398900    42  82     102. 128.   140.   489
## 3 87398950    46  94.2   108. 126.   127.   318
## 4 87398980    40  61      77   85.3   96    228
## 5 87405500    48 102     133  148.   170.   522
## 6 87406900    50 109     134. 154.   170.   670
## 7 87409900    56 112.    156  167.   190.   599
(sum_SolTot_p3 <- plan_wide_19902020 %>%
    select(codigo, solidos_totais, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(solidos_totais, 
            na.rm = TRUE),
      q1 = 
        quantile(solidos_totais, 0.25, 
                 na.rm = TRUE),
      median = 
        median(solidos_totais, 
               na.rm = TRUE),
      mean = 
        mean(solidos_totais, 
             na.rm= TRUE),
      q3 = 
        quantile(solidos_totais, 0.75, 
                 na.rm = TRUE),
      max = 
        max(solidos_totais, 
            na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500    61  69      90   82.8   96    101
## 2 87398900    41  77     104  120.   127    308
## 3 87398950    45  93     101  109.   117    221
## 4 87398980    55  62.8    80   79.9   95    109
## 5 87405500    83  89.2   108. 124.   162.   195
## 6 87406900    50 106     117  135.   163    246
## 7 87409900    75 103     115  131.   145    251

Time for this code chunk to run: 0.324244976043701

ggsave("SolTot.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = SolTot,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p1.png",
       plot = SolTot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p2.png",
       plot = SolTot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p3.png",
       plot = SolTot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")

Time for this code chunk to run: 6.9107129573822

9.0.9 IQA

iqa-gravataí no período 1990-2020Time for this code chunk to run: 1.27456784248352

Time for this code chunk to run: 0.590227127075195

Time for this code chunk to run: 0.628800868988037

Time for this code chunk to run: 0.537429094314575

grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3)

Time for this code chunk to run: 1.61882710456848

(sum_IQA_p1 <- plan_wide_19902020 %>%
   select(codigo, iqa, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(iqa, 
           na.rm = TRUE),
     q1 = 
       quantile(iqa, 0.25, 
                na.rm = TRUE),
     median = 
       median(iqa, 
              na.rm = TRUE),
     mean = 
       mean(iqa, 
            na.rm= TRUE),
     q3 = 
       quantile(iqa, 0.75, 
                na.rm = TRUE),
     max = 
       max(iqa, 
           na.rm = TRUE),
     n = 
        length(iqa)
   )
)
## # A tibble: 7 × 8
##   codigo     min    q1 median  mean    q3   max     n
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500  27.0  35.7   40.9  40.7  46.2  52.2   101
## 2 87398900  27.8  37.9   42.9  43.0  48.0  58.5   101
## 3 87398950  32.8  36.8   41.4  43.2  48.6  61.9    68
## 4 87398980  29.2  35.8   40.4  40.3  44.8  51.9    30
## 5 87405500  24.8  34.9   41.2  40.3  46.9  57.6    97
## 6 87406900  24.7  31.3   37.8  37.4  44.4  49.0    32
## 7 87409900  23.6  31.9   37.1  38.8  46.2  55.4    65
(sum_IQA_p2 <- plan_wide_19902020 %>%
    select(codigo, iqa, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(iqa, 
            na.rm = TRUE),
      q1 = 
        quantile(iqa, 0.25, 
                 na.rm = TRUE),
      median = 
        median(iqa, 
               na.rm = TRUE),
      mean = 
        mean(iqa, 
             na.rm= TRUE),
      q3 = 
        quantile(iqa, 0.75, 
                 na.rm = TRUE),
      max = 
        max(iqa, 
            na.rm = TRUE),
      n = 
        length(iqa)
      )
)
## # A tibble: 7 × 8
##   codigo     min    q1 median  mean    q3   max     n
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500  27.8  34.6   40.0  39.5  43.5  48.7    75
## 2 87398900  28.5  35.1   37.6  38.3  40.6  48.5    77
## 3 87398950  21.1  29.4   32.7  32.8  36.8  44.0    30
## 4 87398980  24.5  35.7   39.4  39.5  43.4  52.1    66
## 5 87405500  19.8  28.7   31.5  31.9  35.7  48.8    78
## 6 87406900  17.1  25.3   29.0  29.5  32.8  44.1    79
## 7 87409900  16.2  20.5   26.1  25.0  29.8  33.1    31
(sum_IQA_p3 <- plan_wide_19902020 %>%
    select(codigo, iqa, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>%
    # ?as_factor(codigo) %>% 
    group_by(codigo) %>%
    summarize(
      min = 
        min(iqa, 
            na.rm = TRUE),
      q1 = 
        quantile(iqa, 0.25, 
                 na.rm = TRUE),
      median = 
        median(iqa, 
               na.rm = TRUE),
      mean = 
        mean(iqa, 
             na.rm= TRUE),
      q3 = 
        quantile(iqa, 0.75, 
                 na.rm = TRUE),
      max = 
        max(iqa, 
            na.rm = TRUE),
      n = 
        length(iqa),
      NAs = 
        sum(is.na(iqa))
      ) %>% 
  mutate(
    "%NA" = NAs/n*100
  )
)
## # A tibble: 7 × 10
##   codigo     min    q1 median  mean    q3   max     n   NAs `%NA`
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl>
## 1 87398500  40.2  42.5   45.4  44.2  45.5  47.2    34    29  85.3
## 2 87398900  34.1  38.6   41.2  40.2  42.9  44.4    36    32  88.9
## 3 87398950  36.7  39.5   42.4  41.5  44.4  44.6    35    31  88.6
## 4 87398980  40.0  40.0   40.0  40.0  40.0  40.0    28    27  96.4
## 5 87405500  30.8  31.6   32.5  32.5  33.3  34.1    33    31  93.9
## 6 87406900  22.9  24.4   25.9  25.3  26.5  27.2    35    32  91.4
## 7 87409900  24.1  25.1   27.3  26.9  28.2  29.7    37    32  86.5

Time for this code chunk to run: 0.249190807342529

ggsave("iqa.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = iqa,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p1.png",
       plot = iqa_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p2.png",
       plot = iqa_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p3.png",
       plot = iqa_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")

Time for this code chunk to run: 6.1524019241333

9.1 Testando coisas

9.1.1 Correlação

parametros_IQA %>% 
  dplyr::select(
    -codigo,
    -ano_coleta,
    -nitrogenio_total
    ) %>% 
  # group_by(codigo) %>% 
  rename(
    
    CE = condutividade,
    E_coli = escherichia_coli,
    OD = oxigenio_dissolvido,
    ST = solidos_totais,
    Turb = turbidez,
    Temp = temperatura_agua,
    Ptot = fosforo_total,
    # NTot = nitrogenio_total,
    NAmon = nitrogenio_amoniacal,
    NTK = nitrogenio_kjeldahl
  ) %>% 
  ggcorr(
    method = "complete.obs",
    # "pearson",
    # "pairwise",
    name = "Correlação",
    label = TRUE,
    label_alpha = TRUE,
    digits = 3,
    low = "#3B9AB2",
    mid = "#EEEEEE",
    high = "#F21A00",
    # palette = "RdYlBu",
    layout.exp = 0,
    legend.position = "left",
    label_round = 3,
    # legend.size = 18,
    geom = "tile",
    nbreaks = 10,
  )+
  labs(title = "Correlação entre parâmetros físico-químicos na\nBacia Hidrográfica do rio Gravataí no período 1990-2020")+
  theme_linedraw()+
  theme(
    legend.position = c(0.15, 0.6),
    legend.title = element_text(size = 16),
    legend.text = element_text(size = 14),
    # legend.spacing = unit(element_text(),
                          # units = 5)
    plot.title = element_text(hjust = 0.5,
                              size = 16)
  )

correlação-parametros-qualidade-agua-gravataí no período 1990-2020

# Gráfico das correlações entre todos os parâmetros com significância
correl_IQA <- parametros_IQA %>%
  dplyr::select(-codigo) %>%
  ggpairs(title = "Correlação entre parâmetros que compõem o IQA",
          axisLabels = "show")

correlacao_pIQA <- parametros_IQA %>% 
  group_by(codigo) %>% 
  correlation::correlation()

correlacao_pIQA %>% 
  # glimpse()
  filter(
    p < 0.001
  ) %>% 
  t() %>% 
  summary()
##  87398500 - oxigenio_dissolvido 87398500 - dbo   87398500 - fosforo_total
##  Min.   :-0.4765                Min.   :0.4176   Min.   :0.3641          
##  1st Qu.: 1.0000                1st Qu.:1.0000   1st Qu.:1.0000          
##  Median : 1.0000                Median :1.0000   Median :1.0000          
##  Mean   : 0.7836                Mean   :0.9552   Mean   :0.8668          
##  3rd Qu.: 1.0000                3rd Qu.:1.0000   3rd Qu.:1.0000          
##  Max.   : 1.0000                Max.   :1.0000   Max.   :1.0000          
##  87398500 - nitrogenio_kjeldahl 87398500 - nitrogenio_total 87398500 - turbidez
##  Min.   :0.4105                 Min.   :0.4915              Min.   :0.3641     
##  1st Qu.:0.7854                 1st Qu.:1.0000              1st Qu.:1.0000     
##  Median :1.0000                 Median :1.0000              Median :1.0000     
##  Mean   :0.8634                 Mean   :0.9609              Mean   :0.9133     
##  3rd Qu.:1.0000                 3rd Qu.:1.0000              3rd Qu.:1.0000     
##  Max.   :1.0000                 Max.   :1.0000              Max.   :1.0000     
##  87398500 - nitrogenio_amoniacal 87398500 - pH 87398500 - temperatura_agua
##  Min.   :1                       Min.   :1     Min.   :-0.4765            
##  1st Qu.:1                       1st Qu.:1     1st Qu.: 1.0000            
##  Median :1                       Median :1     Median : 1.0000            
##  Mean   :1                       Mean   :1     Mean   : 0.8864            
##  3rd Qu.:1                       3rd Qu.:1     3rd Qu.: 1.0000            
##  Max.   :1                       Max.   :1     Max.   : 1.0000            
##  87398500 - solidos_totais 87398500 - escherichia_coli 87398500 - ano_coleta
##  Min.   :0.4915            Min.   :1                   Min.   :-0.3361      
##  1st Qu.:0.8978            1st Qu.:1                   1st Qu.: 1.0000      
##  Median :1.0000            Median :1                   Median : 1.0000      
##  Mean   :0.8976            Mean   :1                   Mean   : 0.8972      
##  3rd Qu.:1.0000            3rd Qu.:1                   3rd Qu.: 1.0000      
##  Max.   :1.0000            Max.   :1                   Max.   : 1.0000      
##  87398500 - condutividade 87398900 - oxigenio_dissolvido 87398900 - dbo  
##  Min.   :1                Min.   :-0.3893                Min.   :0.3880  
##  1st Qu.:1                1st Qu.: 1.0000                1st Qu.:1.0000  
##  Median :1                Median : 1.0000                Median :1.0000  
##  Mean   :1                Mean   : 0.8931                Mean   :0.9529  
##  3rd Qu.:1                3rd Qu.: 1.0000                3rd Qu.:1.0000  
##  Max.   :1                Max.   : 1.0000                Max.   :1.0000  
##  87398900 - fosforo_total 87398900 - nitrogenio_kjeldahl
##  Min.   :0.3526           Min.   :0.4385                
##  1st Qu.:0.5279           1st Qu.:0.9982                
##  Median :1.0000           Median :1.0000                
##  Mean   :0.8295           Mean   :0.9000                
##  3rd Qu.:1.0000           3rd Qu.:1.0000                
##  Max.   :1.0000           Max.   :1.0000                
##  87398900 - nitrogenio_total 87398900 - turbidez
##  Min.   :0.4067              Min.   :0.4067     
##  1st Qu.:0.9982              1st Qu.:0.9070     
##  Median :1.0000              Median :1.0000     
##  Mean   :0.8647              Mean   :0.8634     
##  3rd Qu.:1.0000              3rd Qu.:1.0000     
##  Max.   :1.0000              Max.   :1.0000     
##  87398900 - nitrogenio_amoniacal 87398900 - pH 87398900 - temperatura_agua
##  Min.   :0.3880                  Min.   :1     Min.   :-0.3893            
##  1st Qu.:0.7489                  1st Qu.:1     1st Qu.: 1.0000            
##  Median :1.0000                  Median :1     Median : 1.0000            
##  Mean   :0.8447                  Mean   :1     Mean   : 0.8931            
##  3rd Qu.:1.0000                  3rd Qu.:1     3rd Qu.: 1.0000            
##  Max.   :1.0000                  Max.   :1     Max.   : 1.0000            
##  87398900 - solidos_totais 87398900 - escherichia_coli 87398900 - ano_coleta
##  Min.   :0.4234            Min.   :1                   Min.   :0.3526       
##  1st Qu.:0.9070            1st Qu.:1                   1st Qu.:1.0000       
##  Median :1.0000            Median :1                   Median :1.0000       
##  Mean   :0.8748            Mean   :1                   Mean   :0.9502       
##  3rd Qu.:1.0000            3rd Qu.:1                   3rd Qu.:1.0000       
##  Max.   :1.0000            Max.   :1                   Max.   :1.0000       
##  87398900 - condutividade 87398950 - oxigenio_dissolvido 87398950 - dbo
##  Min.   :1                Min.   :-0.5945                Min.   :1     
##  1st Qu.:1                1st Qu.: 1.0000                1st Qu.:1     
##  Median :1                Median : 1.0000                Median :1     
##  Mean   :1                Mean   : 0.6475                Mean   :1     
##  3rd Qu.:1                3rd Qu.: 1.0000                3rd Qu.:1     
##  Max.   :1                Max.   : 1.0000                Max.   :1     
##  87398950 - fosforo_total 87398950 - nitrogenio_kjeldahl
##  Min.   :0.5497           Min.   :-0.5359               
##  1st Qu.:1.0000           1st Qu.: 1.0000               
##  Median :1.0000           Median : 1.0000               
##  Mean   :0.9307           Mean   : 0.8593               
##  3rd Qu.:1.0000           3rd Qu.: 1.0000               
##  Max.   :1.0000           Max.   : 1.0000               
##  87398950 - nitrogenio_total 87398950 - turbidez
##  Min.   :0.5497              Min.   :0.8455     
##  1st Qu.:1.0000              1st Qu.:1.0000     
##  Median :1.0000              Median :1.0000     
##  Mean   :0.9647              Mean   :0.9881     
##  3rd Qu.:1.0000              3rd Qu.:1.0000     
##  Max.   :1.0000              Max.   :1.0000     
##  87398950 - nitrogenio_amoniacal 87398950 - pH 87398950 - temperatura_agua
##  Min.   :1                       Min.   :1     Min.   :-0.5945            
##  1st Qu.:1                       1st Qu.:1     1st Qu.: 1.0000            
##  Median :1                       Median :1     Median : 1.0000            
##  Mean   :1                       Mean   :1     Mean   : 0.8773            
##  3rd Qu.:1                       3rd Qu.:1     3rd Qu.: 1.0000            
##  Max.   :1                       Max.   :1     Max.   : 1.0000            
##  87398950 - solidos_totais 87398950 - escherichia_coli 87398950 - ano_coleta
##  Min.   :0.8455            Min.   :1                   Min.   :1            
##  1st Qu.:1.0000            1st Qu.:1                   1st Qu.:1            
##  Median :1.0000            Median :1                   Median :1            
##  Mean   :0.9881            Mean   :1                   Mean   :1            
##  3rd Qu.:1.0000            3rd Qu.:1                   3rd Qu.:1            
##  Max.   :1.0000            Max.   :1                   Max.   :1            
##  87398950 - condutividade 87398980 - oxigenio_dissolvido 87398980 - dbo
##  Min.   :-0.4515          Min.   :1                      Min.   :1     
##  1st Qu.: 1.0000          1st Qu.:1                      1st Qu.:1     
##  Median : 1.0000          Median :1                      Median :1     
##  Mean   : 0.8318          Mean   :1                      Mean   :1     
##  3rd Qu.: 1.0000          3rd Qu.:1                      3rd Qu.:1     
##  Max.   : 1.0000          Max.   :1                      Max.   :1     
##  87398980 - fosforo_total 87398980 - nitrogenio_kjeldahl
##  Min.   :1                Min.   :1                     
##  1st Qu.:1                1st Qu.:1                     
##  Median :1                Median :1                     
##  Mean   :1                Mean   :1                     
##  3rd Qu.:1                3rd Qu.:1                     
##  Max.   :1                Max.   :1                     
##  87398980 - nitrogenio_total 87398980 - turbidez
##  Min.   :1                   Min.   :0.5681     
##  1st Qu.:1                   1st Qu.:1.0000     
##  Median :1                   Median :1.0000     
##  Mean   :1                   Mean   :0.9402     
##  3rd Qu.:1                   3rd Qu.:1.0000     
##  Max.   :1                   Max.   :1.0000     
##  87398980 - nitrogenio_amoniacal 87398980 - pH 87398980 - temperatura_agua
##  Min.   :1                       Min.   :1     Min.   :0.5681             
##  1st Qu.:1                       1st Qu.:1     1st Qu.:1.0000             
##  Median :1                       Median :1     Median :1.0000             
##  Mean   :1                       Mean   :1     Mean   :0.9668             
##  3rd Qu.:1                       3rd Qu.:1     3rd Qu.:1.0000             
##  Max.   :1                       Max.   :1     Max.   :1.0000             
##  87398980 - solidos_totais 87398980 - escherichia_coli 87398980 - ano_coleta
##  Min.   :0.6547            Min.   :1                   Min.   :1            
##  1st Qu.:1.0000            1st Qu.:1                   1st Qu.:1            
##  Median :1.0000            Median :1                   Median :1            
##  Mean   :0.9734            Mean   :1                   Mean   :1            
##  3rd Qu.:1.0000            3rd Qu.:1                   3rd Qu.:1            
##  Max.   :1.0000            Max.   :1                   Max.   :1            
##  87398980 - condutividade 87405500 - oxigenio_dissolvido 87405500 - dbo  
##  Min.   :1                Min.   :-0.3314                Min.   :0.4199  
##  1st Qu.:1                1st Qu.: 1.0000                1st Qu.:0.5402  
##  Median :1                Median : 1.0000                Median :1.0000  
##  Mean   :1                Mean   : 0.8976                Mean   :0.7900  
##  3rd Qu.:1                3rd Qu.: 1.0000                3rd Qu.:1.0000  
##  Max.   :1                Max.   : 1.0000                Max.   :1.0000  
##  87405500 - fosforo_total 87405500 - nitrogenio_kjeldahl
##  Min.   :-0.3314          Min.   :0.3930                
##  1st Qu.: 0.4828          1st Qu.:0.6656                
##  Median : 0.7834          Median :0.9319                
##  Mean   : 0.6601          Mean   :0.8206                
##  3rd Qu.: 1.0000          3rd Qu.:1.0000                
##  Max.   : 1.0000          Max.   :1.0000                
##  87405500 - nitrogenio_total 87405500 - turbidez
##  Min.   :0.5052              Min.   :0.3797     
##  1st Qu.:0.6036              1st Qu.:1.0000     
##  Median :0.9033              Median :1.0000     
##  Mean   :0.8197              Mean   :0.9523     
##  3rd Qu.:1.0000              3rd Qu.:1.0000     
##  Max.   :1.0000              Max.   :1.0000     
##  87405500 - nitrogenio_amoniacal 87405500 - pH    87405500 - temperatura_agua
##  Min.   :0.4993                  Min.   :0.3109   Min.   :0.3109             
##  1st Qu.:0.7350                  1st Qu.:0.4284   1st Qu.:0.4534             
##  Median :0.9033                  Median :0.5052   Median :0.5711             
##  Mean   :0.8353                  Mean   :0.6886   Mean   :0.7138             
##  3rd Qu.:1.0000                  3rd Qu.:1.0000   3rd Qu.:1.0000             
##  Max.   :1.0000                  Max.   :1.0000   Max.   :1.0000             
##  87405500 - solidos_totais 87405500 - escherichia_coli 87405500 - ano_coleta
##  Min.   :0.3797            Min.   :1                   Min.   :1            
##  1st Qu.:0.4477            1st Qu.:1                   1st Qu.:1            
##  Median :0.6036            Median :1                   Median :1            
##  Mean   :0.6652            Mean   :1                   Mean   :1            
##  3rd Qu.:1.0000            3rd Qu.:1                   3rd Qu.:1            
##  Max.   :1.0000            Max.   :1                   Max.   :1            
##  87405500 - condutividade 87406900 - oxigenio_dissolvido 87406900 - dbo  
##  Min.   :0.4527           Min.   :-0.5366                Min.   :0.5144  
##  1st Qu.:0.5737           1st Qu.: 1.0000                1st Qu.:0.7408  
##  Median :0.8099           Median : 1.0000                Median :1.0000  
##  Mean   :0.7836           Mean   : 0.6685                Mean   :0.8530  
##  3rd Qu.:1.0000           3rd Qu.: 1.0000                3rd Qu.:1.0000  
##  Max.   :1.0000           Max.   : 1.0000                Max.   :1.0000  
##  87406900 - fosforo_total 87406900 - nitrogenio_kjeldahl
##  Min.   :-0.3894          Min.   :-0.5366               
##  1st Qu.: 0.6154          1st Qu.: 0.6958               
##  Median : 0.7260          Median : 0.9305               
##  Mean   : 0.6906          Mean   : 0.7673               
##  3rd Qu.: 1.0000          3rd Qu.: 1.0000               
##  Max.   : 1.0000          Max.   : 1.0000               
##  87406900 - nitrogenio_total 87406900 - turbidez
##  Min.   :0.3926              Min.   :-0.4238    
##  1st Qu.:0.6754              1st Qu.: 1.0000    
##  Median :0.8658              Median : 1.0000    
##  Mean   :0.8084              Mean   : 0.8905    
##  3rd Qu.:1.0000              3rd Qu.: 1.0000    
##  Max.   :1.0000              Max.   : 1.0000    
##  87406900 - nitrogenio_amoniacal 87406900 - pH    87406900 - temperatura_agua
##  Min.   :0.4212                  Min.   :0.3926   Min.   :-0.3840            
##  1st Qu.:0.7397                  1st Qu.:1.0000   1st Qu.: 0.4569            
##  Median :0.8658                  Median :1.0000   Median : 0.6015            
##  Mean   :0.8237                  Mean   :0.8628   Mean   : 0.6624            
##  3rd Qu.:1.0000                  3rd Qu.:1.0000   3rd Qu.: 1.0000            
##  Max.   :1.0000                  Max.   :1.0000   Max.   : 1.0000            
##  87406900 - solidos_totais 87406900 - escherichia_coli 87406900 - ano_coleta
##  Min.   :0.4459            Min.   :1                   Min.   :-0.4238      
##  1st Qu.:0.6187            1st Qu.:1                   1st Qu.: 1.0000      
##  Median :0.6958            Median :1                   Median : 1.0000      
##  Mean   :0.7839            Mean   :1                   Mean   : 0.8905      
##  3rd Qu.:1.0000            3rd Qu.:1                   3rd Qu.: 1.0000      
##  Max.   :1.0000            Max.   :1                   Max.   : 1.0000      
##  87406900 - condutividade 87409900 - oxigenio_dissolvido 87409900 - dbo  
##  Min.   :0.4024           Min.   :1                      Min.   :0.5624  
##  1st Qu.:0.6399           1st Qu.:1                      1st Qu.:1.0000  
##  Median :0.8068           Median :1                      Median :1.0000  
##  Mean   :0.7870           Mean   :1                      Mean   :0.9371  
##  3rd Qu.:1.0000           3rd Qu.:1                      3rd Qu.:1.0000  
##  Max.   :1.0000           Max.   :1                      Max.   :1.0000  
##  87409900 - fosforo_total 87409900 - nitrogenio_kjeldahl
##  Min.   :0.4560           Min.   :0.6898                
##  1st Qu.:0.8175           1st Qu.:0.9999                
##  Median :0.8565           Median :1.0000                
##  Mean   :0.8426           Mean   :0.9603                
##  3rd Qu.:1.0000           3rd Qu.:1.0000                
##  Max.   :1.0000           Max.   :1.0000                
##  87409900 - nitrogenio_total 87409900 - turbidez
##  Min.   :0.5245              Min.   :0.4405     
##  1st Qu.:0.8314              1st Qu.:1.0000     
##  Median :1.0000              Median :1.0000     
##  Mean   :0.8823              Mean   :0.9570     
##  3rd Qu.:1.0000              3rd Qu.:1.0000     
##  Max.   :1.0000              Max.   :1.0000     
##  87409900 - nitrogenio_amoniacal 87409900 - pH    87409900 - temperatura_agua
##  Min.   :0.7569                  Min.   :0.4049   Min.   :0.4309             
##  1st Qu.:0.8915                  1st Qu.:1.0000   1st Qu.:1.0000             
##  Median :1.0000                  Median :1.0000   Median :1.0000             
##  Mean   :0.9482                  Mean   :0.9542   Mean   :0.9562             
##  3rd Qu.:1.0000                  3rd Qu.:1.0000   3rd Qu.:1.0000             
##  Max.   :1.0000                  Max.   :1.0000   Max.   :1.0000             
##  87409900 - solidos_totais 87409900 - escherichia_coli 87409900 - ano_coleta
##  Min.   :0.4405            Min.   :0.4528              Min.   :0.4528       
##  1st Qu.:0.5429            1st Qu.:0.5245              1st Qu.:1.0000       
##  Median :1.0000            Median :1.0000              Median :1.0000       
##  Mean   :0.8442            Mean   :0.8215              Mean   :0.9579       
##  3rd Qu.:1.0000            3rd Qu.:1.0000              3rd Qu.:1.0000       
##  Max.   :1.0000            Max.   :1.0000              Max.   :1.0000       
##  87409900 - condutividade
##  Min.   :0.4049          
##  1st Qu.:0.4895          
##  Median :0.7254          
##  Mean   :0.7294          
##  3rd Qu.:1.0000          
##  Max.   :1.0000
parametros_IQA %>% 
  # group_by(codigo) %>% 
  dplyr::select(
    nitrogenio_kjeldahl, condutividade
  ) %>% 
  # correlation::cor_test() %>% 
  plot()

correlação-parametros-qualidade-agua-gravataí no período 1990-2020Time for this code chunk to run: 2.73962092399597

9.1.2 Condutividade elétrica

(cond_elet <- ggplot(plan_wide_19902020,
                        aes(codigo,
                            condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
      labs(title = "Condutividade elétrica no período 1990-2020",
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

condutividade-eletrica-gravataí no período 1990-2020Time for this code chunk to run: 1.33914089202881

(cond_elet_p1 <- ggplot(plan_wide_19902020 %>% 
                          filter(ano_coleta>"2000" &
                                   ano_coleta<="2010"),
                        aes(codigo,
                            condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
      labs(title = "Condutividade elétrica no período 1990-2000",
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.630928039550781

(cond_elet_p2 <- ggplot(plan_wide_19902020 %>% 
                          filter(ano_coleta>"2000" &
                                   ano_coleta<="2010"),
                        aes(codigo,
                            condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 2000-2010",
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.727335929870605

(cond_elet_p3 <- ggplot(plan_wide_19902020 %>% 
                          filter(ano_coleta>"2010" &
                                   ano_coleta<="2020"),
                        aes(codigo,
                            condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 2010-2020",
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

Time for this code chunk to run: 0.597105026245117

grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3)

Time for this code chunk to run: 1.82918095588684

(sum_cond_elet_p1 <- plan_wide_19902020 %>%
   select(codigo, condutividade, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(condutividade, 
           na.rm = TRUE),
     q1 = 
       quantile(condutividade, 0.25, 
                na.rm = TRUE),
     median = 
       median(condutividade, 
              na.rm = TRUE),
     mean = 
       mean(condutividade, 
            na.rm= TRUE),
     q3 = 
       quantile(condutividade, 0.75, 
                na.rm = TRUE),
     max = 
       max(condutividade, 
           na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500   9.4  51.1   67    75.1  83.2 340  
## 2 87398900  10    41.5   51    55.3  64.2 160  
## 3 87398950   9    41.5   51.5  60.1  69.5 160  
## 4 87398980  11.3  42.4   52.0  53.0  67.0  83.8
## 5 87405500  25    68.7   88.2 130.  170   560  
## 6 87406900  52    88.4  133.  193.  256.  576  
## 7 87409900  29    80    110.  134.  168.  460
(sum_cond_elet_p2 <- plan_wide_19902020 %>%
    select(codigo, condutividade, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(condutividade, 
               na.rm = TRUE),
      mean = 
        mean(condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(condutividade, 
            na.rm = TRUE))
)
## # A tibble: 7 × 7
##   codigo     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500  11.9  67.0   82.6  84.8 102.   164.
## 2 87398900  11    44.4   52.3  57.1  72.6  136.
## 3 87398950  39.8  58.4   76    82.3  98.3  160 
## 4 87398980   9.4  42.4   49.7  51.5  62    114.
## 5 87405500  17    77.5  107   142.  171.   679 
## 6 87406900  23.1  85.6  124.  164.  199.   619 
## 7 87409900  56.1 114.   177   200.  242    454
(sum_cond_elet_p3 <- plan_wide_19902020 %>%
    select(codigo, condutividade, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(condutividade, 
               na.rm = TRUE),
      mean = 
        mean(condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(condutividade, 
            na.rm = TRUE),
      n = 
        length(condutividade))
)
## # A tibble: 7 × 8
##   codigo     min    q1 median  mean    q3   max     n
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500  0.01  68.5   80.2  80.4  99.5 125.     34
## 2 87398900 39.7   53.4   58.3  61.1  65.5 103      36
## 3 87398950 40.9   64.7   70.1  76.1  82.5 195.     35
## 4 87398980 43.2   51.7   54.0  56.3  61.0  78.9    28
## 5 87405500 47     85.8  121.  146.  209.  286      33
## 6 87406900 62.7   95.9  142.  163.  216.  354.     35
## 7 87409900 65.7  121.   159.  179.  245.  498.     37
# plan_wide_19902020 %>% 
#    select(codigo, IQA) %>% 
#    group_by(codigo) %>% 
#    summarize(
#       min = 
#          min(IQA, 
#              na.rm = TRUE),
#       q1 = 
#          quantile(IQA, 0.25, 
#                   na.rm = TRUE),
#       median = 
#          median(IQA, 
#                 na.rm = TRUE),
#       mean = 
#          mean(IQA, 
#               na.rm= TRUE),
#       q3 = 
#          quantile(IQA, 0.75, 
#                   na.rm = TRUE),
#       max = 
#          max(IQA, 
#              na.rm = TRUE))

Time for this code chunk to run: 0.204574108123779

ggsave("cond_elet.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = cond_elet,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p1.png",
       plot = cond_elet_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p2.png",
       plot = cond_elet_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p3.png",
       plot = cond_elet_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")

Time for this code chunk to run: 6.51275515556335

9.2 Textando o texto

  • § falar do comportamento geral dos dados
  • 2º § - xº § -> abordar os principais parâmetros que estão sendo impactados, detalhando, nas estações mais relevantes, como ficaram os quartis/mediana etc.

10.8

Os resultados apontam que para o parâmetro OD

9.3 Gráficos exemplos boxplot

set.seed(2023)
exemplo_boxplot_df <- data.frame(
  PM = c("PM1"),
  # letras = letters[seq( from = 1, to = 1 )],
  Stat1 = rnorm(100, 
                mean = 5, 
                sd = 1.8)
)

(sumario_exemplo_bp <- exemplo_boxplot_df %>% 
    group_by(PM) %>% 
    summarize(
      max = max(Stat1),
      p95 = quantile(Stat1, 0.95),
      p80 = quantile(Stat1, 0.8),
      median = median(Stat1),
      p20 = quantile(Stat1, 0.2),
      p05 = quantile(Stat1, 0.05),
      min = min(Stat1),
    ) %>% 
    t() %>% 
    row_to_names(row_number = 1) %>% 
    as.numeric()
)
## [1] 9.923430 7.866927 6.683828 4.886935 3.545771 2.277104 1.282177
(boxplot_example <- exemplo_boxplot_df %>% 
    ggplot(
      aes(
        x = PM,
        y = Stat1,
      )
    )+
    stat_summary(
      fun.data = f,
      geom = 'errorbar',
      width = 0.15,
      position = position_dodge(width = 0.65),
    )+
    stat_summary(
      fun.data = f,
      geom = "boxplot",
      width = 0.40,
      fill = '#F8F8FF',
      color = "black",
      outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
    )+
    labs(
      title = "Elementos do *boxplot*",
      x= NULL,
      y= NULL
    )+
    ggbeeswarm::geom_quasirandom(
      size = 1.4,
      alpha = .3,
      width = .07,
    )+
    scale_y_continuous(
      expand = expansion(mult = c(0,0)),
      n.breaks = 8,
      limits = c(-0.3,12)
    )+
    annotate(
      geom = "text",
      x = 1.55,
      hjust = "right",
      y = sumario_exemplo_bp,
      label = c("Valor máximo", "P95", "P80", "Mediana", "P20", "P05", "Valor mínimo"),
      # fontface = 3
    )+
    geom_richtext(
      x = 0.56,
      y = 9.103998,
      label.color = NA,
      hjust = "center",
      label = "<i>Outliers</i>"
    )+
    geom_curve(
      aes(
        x = 0.6, xend = 0.98,
        y = 9.103998 , yend = 9.103998 , #Outliers
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    #definindo o [
    geom_curve(
      aes(
        x = 0.74, xend = 0.78,
        y = 6.683828, yend = 6.683828,
      ),
      curvature = 0,
      size = 1.0,
      lineend = "butt"
    )+
    geom_curve(
      aes(
        x = 0.74, xend = 0.74,
        y = 3.545771, yend = 6.683828,
      ),
      curvature = 0,
      size = 1.0,
      lineend = "butt"
    )+
    annotate(
      geom = "text",
      x = 0.56,
      hjust = "center",
      y = 5.11,
      label = "Amplitude\n(P80-P20)"
    )+
    geom_curve(
      aes(
        x = 0.74, xend = 0.78,
        y = 3.545771 , yend = 3.545771 ,
      ),
      curvature = 0,
      size = 1.0,
      lineend = "butt"
    )+
    # fim do [
    geom_curve(
      aes(
        x = 0.6, xend = 0.90,
        y = 7.866927 , yend = 7.866927 , #whisker superior
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    annotate(
      geom = "text",
      x = 0.56,
      hjust = "center",
      y = 7.866927,
      label = "Whisker\nsuperior"
    )+
    geom_curve(
      aes(
        x = 0.6, xend = 0.90,
        y = 2.277104  , yend = 2.277104  , #whisker inferior
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    annotate(
      geom = "text",
      x = 0.56,
      hjust = "center",
      y = 2.277104,
      label = "Whisker\ninferior"
    )+
    geom_curve(
      aes(
        x = 1.4, xend = 1.01,
        y = 9.92343, yend = 9.92343, #valor máximo
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.11,
        y = 7.866927 , yend = 7.866927 , #P95
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.22,
        y = 6.683828  , yend = 6.683828  , #P80
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.22,
        y = 4.886935   , yend = 4.886935   , #P50
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.22,
        y = 3.545771, yend = 3.545771, #P20
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.11,
        y = 2.277104, yend = 2.277104, #P05
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.4, xend = 1.01,
        y = 1.282177, yend = 1.282177, #valor mínimo
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    # theme_grafs()+
    theme_bw()+
    theme(
      plot.title = 
        element_markdown(
          hjust = 0.5,
          color = "black",
          size = 19),
    )
)

Time for this code chunk to run: 2.35132479667664

ggsave(
  filename = "exemplo_boxplot.png",
  plot = boxplot_example,
  units = c("px"),
  width = (4500)/1.5,
  height = (2993)/1.5,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)

Time for this code chunk to run: 2.47104907035828

set.seed(2021)

data <- tibble(
  grupo = factor(
    c(rep(
      "Grupo 1", 100), 
      rep("Grupo 2", 250), 
      rep("Grupo 3", 25)
    )
  ),
  valor = c(seq(0, 20, length.out = 100),
            c(rep(0, 5), 
              rnorm(30, 2, .1), 
              rnorm(90, 5.4, .1), 
              rnorm(90, 14.6, .1), 
              rnorm(30, 18, .1), 
              rep(20, 5)
            ),
            rep(seq(0, 20, length.out = 5), 5))
) %>% 
  rowwise() %>%
  mutate(
    valor = if_else(
      grupo == "Grupo 2", valor + rnorm(1, 0, .4), 
      valor
      )
    )

## function to return median and labels
n_fun <- function(x){
  return(
    data.frame(
      y = median(x) - 1.25, 
      label = paste0(
        "n = ",length(x)
      )
    )
  )
}

Time for this code chunk to run: 0.152824878692627

(tukey_n_boxplot <- ggplot(data, 
                           aes(x = grupo, 
                               y = valor)
)+
  stat_boxplot(geom = 'errorbar',
               width = 0.15,
               position = position_dodge(width = 0.65))+
  geom_boxplot(fill = "grey92",
               width = 0.40,
               position = position_dodge(width = 0.65))+
  ## use summary function to add text labels
  stat_summary(
    geom = "text",
    fun.data = n_fun,
    # family = "Oswald",
    size = 5
  )+
  labs(
    title = "Tukey *boxplot*",
    x= NULL,
    # y="mg/L"
  )+
  # theme_grafs()+
  theme_bw()+
  theme(
    axis.text.y = element_text(
      angle = 90, 
      # size=15,
      # face=2
    ),
    plot.title = 
      element_markdown(
        hjust = 0.5,
        color = "black",
        size = 19)
  )
)

(tukey_boxplot <- ggplot(data, aes(x = grupo, 
                                   y = valor)) +
  stat_boxplot(geom = 'errorbar',
               width = 0.15,
               position = position_dodge(width = 0.65))+
  geom_boxplot(fill = "grey92",
               width = 0.40,
               position = position_dodge(width = 0.65)) +
  ## use either geom_point() or geom_jitter()
  geom_point(
    ## draw bigger points
    size = 2,
    ## add some transparency
    alpha = .25,
    ## add some jittering
    position = position_jitter(
      ## control randomness and range of jitter
      seed = 1, width = .2
    )
  )+
  theme_bw()+
  labs(
      title = "Tukey *boxplot*",
      x= NULL,
      # y="mg/L"
    )+
  # theme_grafs()+
  theme_bw()+
  theme(
        axis.text.y = element_text(
          angle = 90, 
          # size=15,
          # face=2
        ),
        plot.title = 
          element_markdown(
            hjust = 0.5,
            color = "black",
            size = 19)
    ))

Time for this code chunk to run: 1.10362911224365

data %>% 
  group_by(grupo) %>% 
  summarize(
    min = min(valor),
    P20 = quantile(valor, 0.20),
    q1 = quantile(valor, 0.25),
    mediana = median(valor),
    q3 = quantile(valor, 0.75),
    P80 = quantile(valor, 0.80),
    max = max(valor)
  ) %>% 
  t() %>% 
  row_to_names(row_number = 1)
##         Grupo 1      Grupo 2      Grupo 3     
## min     " 0.0000000" "-0.6345142" " 0.0000000"
## P20     "4.000000"   "5.057189"   "4.000000"  
## q1      "5.000000"   "5.245691"   "5.000000"  
## mediana "10.00000"   "10.01593"   "10.00000"  
## q3      "15.00000"   "14.81205"   "15.00000"  
## P80     "16.00000"   "15.04351"   "16.00000"  
## max     "20.0000"    "20.3882"    "20.0000"
(box_percentile_plot <- ggplot(data, 
       aes(x = grupo, y = valor)
       ) +
      stat_summary(
        fun.data = f,
        geom = 'errorbar',
        width = 0.15,
        position = position_dodge(width = 0.65),
      )+
      stat_summary(
        fun.data = f,
        geom = "boxplot",
        width = 0.40,
        fill = 'grey92',
        color = "black",
        outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
      )+
  # geom_boxplot(fill = "grey92") +
  ## use either geom_point() or geom_jitter()
  geom_point(
    ## draw bigger points
    size = 2,
    ## add some transparency
    alpha = .25,
    ## add some jittering
    position = position_jitter(
      ## control randomness and range of jitter
      seed = 1, width = .2
    )
  )+
  labs(
      title = "*Box Percentile-Plot*",
      x= NULL,
      # y="mg/L"
    )+
  # theme_grafs()+
  theme_bw()+
  theme(
        axis.text.y = element_text(
          angle = 90, 
          # size = 15,
          # face = 2
        ),
        plot.title = 
          element_markdown(
            hjust = 0.5,
            color = "black",
            size = 19)
    )
  )

grid.arrange(
  tukey_boxplot, box_percentile_plot, 
  ncol = 2
  )

fig_tukey_garrett <- plot_grid(tukey_boxplot, box_percentile_plot, 
                               labels = "AUTO")

Time for this code chunk to run: 2.12748694419861

ggsave(
  filename = "tukey_n_boxplot.png",
  plot = tukey_n_boxplot,
  units = c("px"),
  width = 4500,
  height = 2993,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)

ggsave(
  filename = "tukey_boxplot.png",
  plot = tukey_boxplot,
  units = c("px"),
  width = 4500,
  height = 2993,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)

ggsave(
  filename = "box_percentile_plot.png",
  plot = box_percentile_plot,
  units = c("px"),
  width = 4500,
  height = 2993,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)

ggsave(
  filename = "fig_tukey_garrett.png",
  plot = fig_tukey_garrett,
  units = c("px"),
  width = 4500,
  height = 2993,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)

Time for this code chunk to run: 3.09082198143005

---
title: "TCC"
author: "Leonardo Fernandes Wink"
date: "`r format(Sys.time(), '%d/%m/%Y')`"
output:
  html_document: 
    distill::distill_article:
    highlight: haddock
    keep_md: yes
    number_sections: yes
    theme: flatly
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: no
    fig_width: 10
    fig_height: 6.66
    fig_caption: yes
    code_download: true
  pdf_document:
    toc: yes
  word_document: 
    toc: yes
    keep_md: yes
  github_document:
    html_preview: true
always_allow_html: yes
editor_options: 
  chunk_output_type: console
fig.align: center
---

```{r Rotina pra toda vez que abrir o documento, echo = FALSE}
# Abrir o GitHub Desktop
# Verificar se há pull pra ser feito
# Abrir o RStudio
```

# Brief explanation

Every boxplot means a monitoring point (Ponto de monitoramento (or PM) in portuguese). My goal here is to analyze the evolution between decades of each water quality parameter that compounds the Water Quality Index (WQI).

The river flows in the east-west direction as shown in the image below.

![](images/paste-7AD7027F.png)

The logic behind the sorting in the boxplots is because of 2 main reasons:

1.  The original monitoring point isn't easy to understand (8 digits, like 87409900)
2.  Changing the original nomenclature to PM1, PM2 (...) makes it easier to understand that the last point has water contributions of every other point upstream.

Some features that I want to add:

-   If the parameter is x, then use x's classes (with its own classes background color plotted)

-   Define the timescale, should act just like a filter

```{r p1 example, eval=FALSE}
# plan_wide_19902020 %>%
#   filter(ano_coleta > "1990" &
#          ano_coleta <= "2000")
```

# Anotações de coisas por fazer:

-   Descobrir como colocar as estações no sentido correto montante -\> jusante nos sumários

> 87398500, 87398980, 87398900, 87398950, 87405500, 87406900, 87409900

-   ~~Aprender a segmentar o meu dataset por períodos~~
-   aprender a criar uma nova coluna com a segmentação dos períodos
-   maybe use `~facet.grid`
-   aprender a colocar a legenda dentro do gráfico
    -   reduzir o tamanho da legenda
-   ~~corrigir os valores 0 de IQA pra NA~~
-   descobrir como conseguir a equação do lm
-   ~~aprender a pivotar o sumário~~ -\> meu sumário do google docs ta batendo direitinho com o do R
-   descobrir se há outros TCCs com disponibilização de códigos
-   ~~`Namon` tá com com casa decimal `","` e `ptot` tá com `"."`~~
-   correlação forte entre condutividade e Namon/Ptot/DBO

| 1990-2000 | 2000-2010 | 2010-2020 |
|:---------:|:---------:|:---------:|
| 1990-2000 | 2000-2010 | 2010-2020 |

# Instalar os pacotes

```{r instalar pacotes, eval=FALSE}
# install.packages(tidyverse)
```

## acessar os pacotes

```{r Acessar os pacotes, message = FALSE, warning = TRUE}
# library(ggpubr)
pacman::p_load(readr, rmarkdown, readxl, janitor,
               pillar, dplyr, tidyverse,
               # gapminder, 
               knitr, kableExtra, see,
               gridExtra, #modelsummary, 
               gtsummary, ggplot2,
               ggbeeswarm, GGally, ggtext, cowplot,
               report)
# pacman::p_load(tibbletime)
# cite_packages()
```

```{r cronometrando quanto tempo cada chunk leva}
knitr::knit_hooks$set(time_it = local({
   now <- NULL
   function(before, options) {
      if (before) {
         # record the current time before each chunk
         now <<- Sys.time()
      } else {
         # calculate the time difference after a chunk
         res <- difftime(Sys.time(), now)
         # return a character string to show the time
         paste("Time for this code chunk to run:", res)
      }
   }
}))

knitr::opts_chunk$set(time_it = TRUE)
```

```{r setup, include=FALSE}
# knitr::opts_chunk$set(echo = TRUE)
```

### referenciando os pacotes

```{r referenciando os pacotes}
# version$version.string
# citation(package = "tidyverse")
```

## importando a planilha

```{r Importando a planilha, echo = FALSE, message = TRUE, warning = FALSE}
plan_wide_19902020 <- read_delim("https://raw.githubusercontent.com/leonardofwink/TCC_gh/main/plan_wide_19902020.tsv",
                                 delim = "\t", 
                                 escape_double = FALSE,
                                 col_types = cols(
                                   Alcalinidade = col_double(),
                                   CODIGO = col_character(), 
                                   COORD_GEO_LAT_GRAU = col_double(),
                                   COORD_GEO_LONG_GRAU = col_double(),
                                   DATA_COLETA = col_date(format = "%d/%m/%Y"),
                                   Nitrato = col_double(), 
                                   Nitrito = col_double(),
                                   SDT = col_double(), 
                                   SST = col_double(),
                                   `Vazao` = col_double(), 
                                   `Vazao rio` = col_double()
                                 ),
                                 locale = locale(
                                   date_names = "pt", 
                                   decimal_mark = ",",
                                   grouping_mark = ""
                                 ),
                                 trim_ws = TRUE
) %>% 
  janitor::clean_names() %>% 
  rename(
    pH = p_h,
    iqa_pH = iqa_p_h,
    iqa_pH_2 = iqa_p_h_2
  ) %>% 
  mutate(
    ponto_monitoramento = case_when(
      codigo == "87398500" ~ "PM1",
      codigo == "87398980" ~ "PM2",
      codigo == "87398900" ~ "PM3",
      codigo == "87398950" ~ "PM4",
      codigo == "87405500" ~ "PM5",
      codigo == "87406900" ~ "PM6",
      codigo == "87409900" ~ "PM7"
    )
  ) %>% 
  select(codigo, ponto_monitoramento, everything()) #reordenando colunas
# teste <- plan_wide_19902020 %>%
#   dplyr::filter(data_coleta >= as.POSIXct("2010-01-01")) #this works
```

```{r Visualização da planilha importada, echo = FALSE}
paged_table(plan_wide_19902020,
            options = list(rows.print = 15,
                           cols.print = 10))
```

# data wrangling

Como há dados faltantes, no cálculo entre o produto das colunas, o R acaba interpretando como se fosse zero, mas na verdade é `NA`.

```{r data wrangling}
plan_wide_19902020 <- plan_wide_19902020 %>% 
   mutate(iqa = ifelse(iqa == 0, NA, iqa))

parametros_IQA <- plan_wide_19902020 %>%
  select(
    codigo,
    ponto_monitoramento,
    pH,
    oxigenio_dissolvido,
    dbo,
    fosforo_total,
    escherichia_coli,
    nitrogenio_amoniacal,
    nitrogenio_kjeldahl,
    nitrogenio_total,
    turbidez,
    temperatura_agua,
    solidos_totais,
    condutividade,
    ano_coleta
  )

write.csv(parametros_IQA,
          "./parametros_IQA.csv",
          row.names = FALSE)

plan_wide_19902020 %>% 
  select(starts_with("iqa_")) %>% 
  mutate(
    teste_iqa_calc = prod() #queria tentar gerar o produtório entre as colunas que já possuem o IQA^2
  )
```

```{r Códigos Git, echo = FALSE}
# cd myrepo
# ls
# head README.md
# git status
# git add README.md
# git commit -m "A commit from my local computer"
# 
# cd .. # voltar pro diretório acima
# rm -rf myrepo/ #remover/apagar a pasta myrepo
```

```{r Aprendendo Git, echo = FALSE}
# slides da bia que ajudam mt
# https://beatrizmilz.github.io/slidesR/git_rstudio/11-2021-ENCE.html#20
# aprendendo a sincronizar usando esse guia -> 
# https://happygitwithr-com.translate.goog/push-pull-github.html?_x_tr_sl=auto&_x_tr_tl=pt&_x_tr_hl=pt-BR
# library(usethis)
# usethis::create_github_token() criar um código pra acesso e sincronização between R e github

# gitcreds::gitcreds_set() 
# 
# use_git_config(user.name = "leonardofwink",
#                user.email = "leonardofwink@gmail.com")
# usethis::gh_token_help()

# Como mostrar os dados de um arquivo via Git/GitHub
# git clone https://github.com/leonardofwink/myrepo.git
# cd myrepo #acessa a pasta myrepo
# ls #lista os arquivos da pasta 
# head README.md #mostra as primeiras observações do arquivo

# Como mostrar os dados de um arquivo via R
# head(C:/Users/Léo/myrepo/README.md)

# Adicionar uma linha ao README.md e verificar se o Git percebe a mudança
# echo "A line I wrote on my local computer" >> README.md
# git status
## C:\Users\Léo\myrepo>git status
## On branch main
## Your branch is up to date with 'origin/main'.
## 
## Changes not staged for commit:
##   (use "git add <file>..." to update what will be committed)
##   (use "git restore <file>..." to discard changes in working directory)
##         **modified:   README.md**
## 
## no changes added to commit (use "git add" and/or "git commit -a")
```

# setting theme

```{r setting theme}
theme_grafs <- function(bg = "white", 
                        coloracao_letra = "black") {
  theme(
    plot.title = 
      element_text(
        hjust = 0.5,
        color = coloracao_letra,
        size = 19),
    
    axis.title.x = 
      # element_text(
      # color = coloracao_letra,
      # size = 15,
      # angle = 0,),
      element_blank(),
    axis.title.y = element_text(
      color = coloracao_letra,
      size = 15,
      angle = 90),
    
    axis.text.x = element_text(
      color = coloracao_letra,
      size = 17),
    axis.text.y = element_text(
      color = coloracao_letra,
      size = 17,
      angle = 0),
    
    strip.background = element_rect(fill = bg,
                                    linetype = 1,
                                    size = 0.5,
                                    color = "black"),
    strip.text = element_text(size = 17),
    panel.background = element_rect(fill = bg),
    plot.background = element_rect(fill = bg),
    plot.margin = margin(l = 5, r = 10,
                         b = 5, t = 5)
  )
}
```

# setting different timescales

```{r setting periodos, echo = FALSE}
plan_wide_19902020 <- plan_wide_19902020 %>% 
  mutate(
    periodo = if_else(
      ano_coleta <= 2000, 
      "1990-2000",
      if_else(
        ano_coleta <= 2010,
        "2000-2010",
        "2010-2020"
      )
    )
  )
```

# setting sumaries

```{r Sumários, echo = FALSE}
sumario <- function(dados = plan_wide_19902020, parametro = "") {
  dados %>% 
    # filter(ano_coleta>"1990" &
    #          ano_coleta<="2000") %>%
    group_by(ponto_monitoramento) %>%
    summarize(
      min =
        min(parametro,
            na.rm = TRUE),
      p05 = 
        quantile(parametro, 0.05,
                 na.rm = TRUE),
      p20 =
        quantile(parametro, 0.20,
                 na.rm = TRUE),
      median =
        median(parametro,
               na.rm = TRUE),
      mean =
        mean(parametro,
             na.rm= TRUE),
      p80 =
        quantile(parametro, 0.80,
                 na.rm = TRUE),
      p95 =
        quantile(parametro, 0.95,
                 na.rm = TRUE),
      max =
        max(parametro,
            na.rm = TRUE)
      )
}
```


# Funções 
## criando função para gerar boxplots com percentil 20 e 80

```{r funcao percentil 80}
f <- function(x) {
  r <- quantile(x, probs = c(0.05, 0.20, 0.50, 0.80, 0.95))
  names(r) <- c("ymin", "lower", "middle", "upper", "ymax")
  return(r)
}
```

## criando função para gerar gráfico de od
```{r gerando function graf od, echo = FALSE}
boxplot_od <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = oxigenio_dissolvido, titulo = "Oxigênio Dissolvido"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin = -Inf, ymax = 2,
             alpha = 1,
             fill = "#ac5079")+ #>pior classe
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin = 2, ymax = 4,
             alpha = 1,
             fill = "#eb5661")+ #classe 4
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin = 4, ymax = 5,
             alpha = 1,
             fill = "#fcf7ab")+ #classe 3
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin = 5, ymax = 6,
             alpha = 1,
             fill = "#70c18c")+ #classe 2
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin= 6, ymax = Inf,
             alpha = 1,
             fill = "#8dcdeb")+ #classe 1
    stat_summary(
      fun.data = f,
      geom = 'errorbar',
      width = 0.3,
      position = position_dodge(width = 0.65),
    )+
    stat_summary(
      fun.data = f,
      geom = "boxplot",
      width = 0.7,
      fill = '#F8F8FF',
      color = "black",
      outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
    )+
    # facet_wrap(~periodo)+
    labs(
      title = titulo,
      x= NULL,
      y="mg/L"
    )+
    ggbeeswarm::geom_quasirandom(
      size = 1.2,
      alpha = .25,
      width = .07,
    )+
    scale_y_continuous(
      expand = expansion(mult = c(0,0)),
      n.breaks = 11,
      limits = c(-0.3,21)
    )+
    scale_x_discrete(limits = c("87398500",
                                "87398980",
                                "87398900",
                                "87398950",
                                "87405500",
                                "87406900",
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(
      method = "lm",
      se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
      aes(group = 1),
      alpha = .5,
      na.rm = TRUE,
      size = 1
    )+
    theme_grafs()
}
```

## criando função para gerar gráfico de dbo
```{r gerando function graf dbo, echo = FALSE}
boxplot_dbo <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = dbo, titulo = "Demanda Bioquímica de Oxigênio"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
    annotate("rect",
             xmin=-Inf, xmax=Inf,
             ymin=10, ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf, xmax=Inf,
             ymin=5, ymax=10,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf, xmax=Inf,
             ymin=3, ymax=5,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf, xmax=Inf,
             ymin=0, ymax=3,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_summary(
      fun.data = f,
      geom = 'errorbar',
      width = 0.3,
      position = position_dodge(width = 0.65),
    )+
    stat_summary(
      fun.data = f,
      geom = "boxplot",
      width = 0.7,
      fill = '#F8F8FF',
      color = "black",
      outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
    )+
    # facet_wrap(~periodo)+
    labs(title = titulo,
         x="Estação",
         y="mg/L",
         caption = "Leonardo Fernandes Wink"
    )+
    ggbeeswarm::geom_quasirandom(
      size = 1.2,
      alpha = .25,
      width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(1,100),
                       trans = "log10")+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
}
```

## Ptot
```{r gerando function graf ptot, echo = FALSE}
boxplot_ptot <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = fosforo_total, titulo = "Fósforo total"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
  stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
    labs(title = titulo,
         x="Estação",
         y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                 max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " ")
                      )+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
}
```

## E coli
```{r funcao-graf-ecoli, echo = FALSE}
boxplot_ecoli <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = escherichia_coli, titulo = "*Escherichia coli*"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=3200, ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=800, ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=160, ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=0, ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   # facet_wrap(~periodo)+
   labs(title = titulo,
        x="Estação",
        y="NMP/100mL")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      # n.breaks = 9,
                      n.breaks = 6,
                      limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                 max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()+
    theme(
        axis.text.y = element_text(
          angle = 90, 
          # size=15,
          # face=2
        ),
        plot.title = 
          element_markdown(
            hjust = 0.5,
            color = "black",
            size = 19)
    )
}
```

## Nitrogênio Amoniacal
```{r}
boxplot_namon <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = nitrogenio_amoniacal, titulo = "Nitrogênio Amoniacal"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   # facet_wrap(~periodo)+
   labs(title = titulo,
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE),
                                 max(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
}
```

## Turbidez

```{r}
boxplot_turb <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = turbidez, titulo = "Turbidez"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
    annotate("rect",
             xmin=-Inf, xmax=Inf,
             ymin=100, ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf, xmax=Inf,
            ymin=40, ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=0, ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   labs(title = titulo,
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.05)),
                      n.breaks = 8,
                      limits = c(
                        # 1,
                        min(plan_wide_19902020$turbidez, na.rm = TRUE),
                        # 500
                        max(plan_wide_19902020$turbidez, na.rm = TRUE)
                      ),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
    theme_grafs()
}

```


## pH

## Sólidos Totais

## Condutividade

# Parâmetros físico-químicos

### Oxigênio Dissolvido

```{r Gráfico OD facetted, echo = FALSE, warning=FALSE, message = FALSE, fig.cap="Oxigênio Dissolvido no período 1990-2020"}
(od <- plan_wide_19902020 %>% 
   boxplot_od(
     titulo = "Oxigênio Dissolvido no período 1990-2020"
   )+
   facet_wrap(~periodo)
)
```

```{r Gráfico OD periodo 1, echo = FALSE, warning=FALSE, message = FALSE, fig.cap="Oxigênio Dissolvido no período 1990-2000"}
(od_p1 <- plan_wide_19902020 %>% 
   filter(ano_coleta > "1990" &
            ano_coleta <= "2000") %>% 
   boxplot_od(
     titulo = "Oxigênio Dissolvido no período 1990-2000"
   )
)
```

```{r Gráfico OD periodo 2, echo = FALSE, warning=FALSE, message = FALSE}
(od_p2 <- plan_wide_19902020 %>% 
   filter(ano_coleta > "2000" &
            ano_coleta <= "2010") %>% 
   boxplot_od(
     titulo = "Oxigênio Dissolvido no período 2000-2010"
   )
)
```

```{r Gráfico OD periodo 3, echo = FALSE, warning=FALSE, message = FALSE}
(od_p3 <- plan_wide_19902020 %>% 
   filter(ano_coleta > "2010" &
            ano_coleta <= "2020") %>% 
   boxplot_od(
     titulo = "Oxigênio Dissolvido no período 2010-2020"
   )
)
```

```{r Salvando OD, warning=FALSE, message = FALSE,}
ggsave("od.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = od,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p1.png",
       plot = od_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p2.png",
       plot = od_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p3.png",
       plot = od_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

```{r Gráfico IQA OD periodo1, echo = FALSE, message=FALSE, warning=FALSE}
(iqaod_p1 <-ggplot(plan_wide_19902020 %>% 
                      filter(ano_coleta > "1990" &
                                ano_coleta <= "2000"),
                   aes(codigo,
                       iqa_od, na.rm = TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 1990-2000",
        x="Estação",
         y="")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    geom_smooth(
       method = "lm",
       se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
       aes(group=1),
       alpha=.5,
       na.rm = TRUE,
       size = 1
    )+
    theme_grafs()
)
```

```{r Gráfico IQA OD periodo2, echo = FALSE, warning= FALSE, message = FALSE}
(iqaod_p2 <-ggplot(plan_wide_19902020 %>% 
                      filter(ano_coleta > "2000" &
                                ano_coleta <= "2010"),
                   aes(codigo,
                       iqa_od, na.rm = TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7,
                 na.rm = TRUE)+
    labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 2000-2010",
         x="Estação",
         y="")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(
       method = "lm",
       se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
       aes(group=1),
       alpha=.5,
       na.rm = TRUE,
       size = 1
    )+
    theme_grafs()
)

```

```{r Gráfico IQA OD periodo3, echo = FALSE, warning=FALSE, message = FALSE}
(iqaod_p3 <-ggplot(plan_wide_19902020 %>% 
                      filter(ano_coleta > "2010" &
                                ano_coleta <= "2020"),
                   aes(codigo,
                       iqa_od, na.rm = TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7,
                 na.rm = TRUE)+
    labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 2010-2020",
         x="Estação",
         y="")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(
       method = "lm",
       se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
       aes(group=1),
       alpha=.5,
       na.rm = TRUE,
       size = 1
    )+
    theme_grafs()
)
```

```{r Sumário OD, echo = FALSE, warning=FALSE, message = FALSE,}
(sum_od_p1 <- plan_wide_19902020 %>%
    select(codigo, oxigenio_dissolvido, ano_coleta) %>% 
    filter(ano_coleta > "1990" &
             ano_coleta <= "2000") %>% 
   group_by(codigo) %>% 
   # codigo == "87398500" <- "teste1"
    # %>% 
 summarize(
       max = 
         max(oxigenio_dissolvido, na.rm = TRUE),
       p95 = 
         quantile(oxigenio_dissolvido, 0.95, na.rm = TRUE),
       p80 = 
         quantile(oxigenio_dissolvido, 0.80, na.rm = TRUE),
       median = 
         median(oxigenio_dissolvido, na.rm = TRUE),
       mean = 
         mean(oxigenio_dissolvido, na.rm= TRUE),
       p20 = 
         quantile(oxigenio_dissolvido, 0.20, na.rm = TRUE),
       p05 = 
         quantile(oxigenio_dissolvido, 0.05, na.rm = TRUE),
       min = 
         min(oxigenio_dissolvido, na.rm = TRUE),
       n = 
         length(oxigenio_dissolvido)
 ) %>% 
    pivot_longer(
       !codigo,
       names_to = "par",
       values_to = "valor"
    ) %>% 
    pivot_wider(names_from = codigo,
                values_from = valor) %>% 
   rename(
     "PM1" = "87398500",
     "PM2" = "87398900",
     "PM3" = "87398950",
     "PM4" = "87398980",
     "PM5" = "87405500",
     "PM6" = "87406900",
     "PM7" = "87409900"
   ) 
 )

# teste1 <- parametros_IQA %>% 
#   group_by(codigo) %>% 
#   pivot_longer(
#     !codigo,
#     names_to = "parametro",
#     values_to = "valor"
#   ) %>% 
#   # group_by(parametro)
#   pivot_wider(
#     names_from = codigo,
#     values_from = valor,
#     # .groups = "drop"
#   ) %>% 
#   rename(
#     "PM1" = "87398500",
#     "PM2" = "87398900",
#     "PM3" = "87398950",
#     "PM4" = "87398980",
#     "PM5" = "87405500",
#     "PM6" = "87406900",
#     "PM7" = "87409900"
#   ) %>%
#   select(par, PM1, PM2, PM3, PM4, PM5, PM6, PM7) %>% 
#   filter(
#     par == "pH"
#   ) 
# %>% 
#   unnest(dplyr::everything())


# teste1$PM1[2]

# %>%
#   summarize(
#     max =
#       max(oxigenio_dissolvido, na.rm = TRUE),
#     q3 =
#       quantile(oxigenio_dissolvido, 0.75, na.rm = TRUE),
#     median =
#       median(oxigenio_dissolvido, na.rm = TRUE),
#     mean =
#       mean(oxigenio_dissolvido, na.rm= TRUE),
#     q1 =
#       quantile(oxigenio_dissolvido, 0.25, na.rm = TRUE),
#     min =
#       min(oxigenio_dissolvido, na.rm = TRUE),
#     n =
#       length(oxigenio_dissolvido)
#     )
# #     
# 
# sum(sum_od_p1$n)



(sum_od_p2 <- plan_wide_19902020 %>%
      select(codigo, oxigenio_dissolvido, ano_coleta) %>% 
      filter(ano_coleta>"2000" &
                ano_coleta<="2010") %>% 
      group_by(codigo) %>% 
      summarize(
         min = 
            min(oxigenio_dissolvido, na.rm = TRUE),
         q1 = 
            quantile(oxigenio_dissolvido, 0.25, na.rm = TRUE),
         median = 
            median(oxigenio_dissolvido, na.rm = TRUE),
         mean = 
            mean(oxigenio_dissolvido, na.rm= TRUE),
         q3 = 
            quantile(oxigenio_dissolvido, 0.75, na.rm = TRUE),
         max = 
            max(oxigenio_dissolvido, na.rm = TRUE)
      )
)

(sum_od_p3 <- plan_wide_19902020 %>%
      select(codigo, oxigenio_dissolvido, ano_coleta) %>% 
      filter(ano_coleta>"2010" &
                ano_coleta<="2020") %>% 
      group_by(codigo) %>% 
      summarize(
         min = 
            min(oxigenio_dissolvido, na.rm = TRUE),
         q1 = 
            quantile(oxigenio_dissolvido, 0.25, na.rm = TRUE),
         median = 
            median(oxigenio_dissolvido, na.rm = TRUE),
         mean = 
            mean(oxigenio_dissolvido, na.rm= TRUE),
         q3 = 
            quantile(oxigenio_dissolvido, 0.75, na.rm = TRUE),
         max = 
            max(oxigenio_dissolvido, na.rm = TRUE)
      )
)

#   pivot_wider(id_cols = codigo,
#               names_from = codigo,
#               values_from = oxigenio_dissolvido)
# 
# 
#   group_by(codigo) %>%
#   get_summary_stats(type = "common") %>%
#   pivot_wider(id_cols = variable,
#               names_from = codigo,
#               values_from = variable$oxigenio_dissolvido)
# 
# # install.packages("ggpubr")
# # library(ggpubr)
```

### Demanda Bioquímica de Oxigênio

```{r Gráfico DBO facetted, fig.cap="Demanda Bioquímica de Oxigênio no período 1990-2020"}
(dbo <- ggplot(plan_wide_19902020,
               aes(x = codigo,
                   y = dbo))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=10,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=10,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 1990-2020",
        x="Estação",
        y="mg/L",
        # caption = "Leonardo Fernandes Wink"
        )+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(1,100),
                      trans = "log10")+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico DBO período1, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p1<-ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta>"1990" &
                             ano_coleta<="2000"),
                aes(codigo,
                    dbo))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=10,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=5,
             ymax=10,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3,
             ymax=5,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=3,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Demanda Bioquímica de Oxigênio no período 1990-2000",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(1,100),
                       trans = "log10")+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico DBO período2, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p2<-ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta>"2000" &
                             ano_coleta<="2010"),
                aes(codigo,
                    dbo))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=10,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=5,
             ymax=10,
             alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(1,100),
                       trans = "log10")+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico DBO período3, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p3<-ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta>"2010" &
                             ano_coleta<="2020"),
                aes(codigo,
                    dbo, na.rm=TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=10,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=5,
             ymax=10,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3,
             ymax=5,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=3,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Demanda Bioquímica de Oxigênio no período 2010-2020",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(1,100),
                       trans = "log10")+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
        geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico IQA DBO periodo1, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo1<-ggplot(plan_wide_19902020 %>% 
                    filter(ano_coleta>"1990" &
                             ano_coleta<="2000"),
                  aes(codigo,
                      iqa_dbo))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1))
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Variação do IQA para o parâmetro DBO 1990-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico IQA DBO periodo2, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo2<-ggplot(plan_wide_19902020%>% 
                     filter(ano_coleta>"2000" &
                               ano_coleta<="2010"),
                  aes(codigo,
                      iqa_dbo))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1))
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Variação do IQA para o parâmetro DBO 2000-2010",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico IQA DBO periodo3, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo3<-ggplot(plan_wide_19902020%>% 
                     filter(ano_coleta>"2010" &
                               ano_coleta<="2020"),
                  aes(codigo,
                      iqa_dbo))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1))
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Variação do IQA para o parâmetro DBO 2010-2020",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
        geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Sumário DBO, warning=FALSE, message = FALSE,}
(sum_dbo_p1 <- plan_wide_19902020 %>%
   select(codigo, dbo, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(dbo, 
           na.rm = TRUE),
     q1 = 
       quantile(dbo, 0.25, 
                na.rm = TRUE),
     median = 
       median(dbo, 
              na.rm = TRUE),
     mean = 
       mean(dbo, 
            na.rm= TRUE),
     q3 = 
       quantile(dbo, 0.75, 
                na.rm = TRUE),
     max = 
       max(dbo, 
           na.rm = TRUE))
)

(sum_dbo_p2 <- plan_wide_19902020 %>%
    select(codigo, dbo, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(dbo, 
            na.rm = TRUE),
      q1 = 
        quantile(dbo, 0.25, 
                 na.rm = TRUE),
      median = 
        median(dbo, 
               na.rm = TRUE),
      mean = 
        mean(dbo, 
             na.rm= TRUE),
      q3 = 
        quantile(dbo, 0.75, 
                 na.rm = TRUE),
      max = 
        max(dbo, 
            na.rm = TRUE))
)

(sum_dbo_p3 <- plan_wide_19902020 %>%
    select(codigo, dbo, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(dbo, 
            na.rm = TRUE),
      q1 = 
        quantile(dbo, 0.25, 
                 na.rm = TRUE),
      median = 
        median(dbo, 
               na.rm = TRUE),
      mean = 
        mean(dbo, 
             na.rm= TRUE),
      q3 = 
        quantile(dbo, 0.75, 
                 na.rm = TRUE),
      max = 
        max(dbo, 
            na.rm = TRUE))
)
```

```{r Salvando DBO, warning=FALSE, message = FALSE,}
ggsave("dbo.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = dbo,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p1.png",
       plot = dbo_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p2.png",
       plot = dbo_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p3.png",
       plot = dbo_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Fósforo total

```{r Gráfico fósforo total facetted, fig.cap="Fósforo total no período 1990-2020"}
(ptot <- ggplot(plan_wide_19902020,
                aes(codigo,
                    fosforo_total))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
  stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
  facet_wrap(~periodo)+
    labs(title = "Fósforo total no período 1990-2020",
         x="Estação",
         y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                 max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " ")
                      )+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico Fósforo total periodo1, warning = FALSE, message = FALSE}
(ptot_p1<-ggplot(plan_wide_19902020%>% 
                   filter(ano_coleta>"1990" &
                             ano_coleta<="2000"),
                 aes(codigo,
                     fosforo_total))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.15,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.1,
             ymax=0.15,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=0.1,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Fósforo total no período 1990-2000",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                  max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                       trans = "log10")+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

```

```{r Gráfico Fósforo total periodo2, warning = FALSE, message = FALSE}
(ptot_p2 <- ggplot(plan_wide_19902020%>% 
                      filter(ano_coleta>"2000" &
                                ano_coleta<="2010"),
                   aes(codigo,
                       fosforo_total))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.15,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.1,
             ymax=0.15,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=0.1,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Fósforo total no período 2000-2010",
         x="Estação",
         y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                 max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                      trans = "log10")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

```

```{r Gráfico Fósforo total periodo3, warning = FALSE, message = FALSE}
(ptot_p3 <- ggplot(plan_wide_19902020%>% 
                      filter(ano_coleta>"2010" &
                                ano_coleta<="2020"),
                   aes(codigo,
                       fosforo_total))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.15,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.1,
             ymax=0.15,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=0.1,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Fósforo total no período 2010-2020",
         x="Estação",
         y="mg/L")+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                  max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                       trans = "log10")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

```

```{r Sumário Fósforo total, warning=FALSE, message = FALSE,}
(sum_ptot_p1 <- plan_wide_19902020 %>%
    select(codigo, fosforo_total, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(fosforo_total, na.rm = TRUE),
     q1 = 
       quantile(fosforo_total, 0.25, na.rm = TRUE),
     median = 
       median(fosforo_total, na.rm = TRUE),
     mean = 
       mean(fosforo_total, na.rm= TRUE),
     q3 = 
       quantile(fosforo_total, 0.75, na.rm = TRUE),
     max = 
       max(fosforo_total, na.rm = TRUE)))

(sum_ptot_p2 <- plan_wide_19902020 %>%
    select(codigo, fosforo_total, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(fosforo_total, na.rm = TRUE),
      q1 = 
        quantile(fosforo_total, 0.25, na.rm = TRUE),
      median = 
        median(fosforo_total, na.rm = TRUE),
      mean = 
        mean(fosforo_total, na.rm= TRUE),
      q3 = 
        quantile(fosforo_total, 0.75, na.rm = TRUE),
      max = 
        max(fosforo_total, na.rm = TRUE)))

(sum_ptot_p3 <- plan_wide_19902020 %>%
    select(codigo, fosforo_total, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(fosforo_total, na.rm = TRUE),
      q1 = 
        quantile(fosforo_total, 0.25, na.rm = TRUE),
      median = 
        median(fosforo_total, na.rm = TRUE),
      mean = 
        mean(fosforo_total, na.rm= TRUE),
      q3 = 
        quantile(fosforo_total, 0.75, na.rm = TRUE),
      max = 
        max(fosforo_total, na.rm = TRUE)))

```

```{r Salvando Ptot, warning=FALSE, message = FALSE,}
ggsave("ptot.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = ptot,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p1.png",
       plot = ptot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p2.png",
       plot = ptot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p3.png",
       plot = ptot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Escherichia coli

```{r Gráfico Ecoli facetted, fig.cap="Escherichia-coli-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(ecoli <- boxplot_ecoli(
  titulo = "*Escherichia coli* no período 1990-2020"
)+
  facet_wrap(~periodo)
)

(ecoli <- ggplot(plan_wide_19902020,
                 aes(codigo,
                     escherichia_coli))+
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=3200, ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=800, ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=160, ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=0, ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "*Escherichia coli* no período 1990-2020",
        x="Estação",
        y="NMP/100mL")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      # n.breaks = 9,
                      n.breaks = 6,
                      limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                 max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()+
    theme(
        axis.text.y = element_text(
          angle = 90, 
          # size=15,
          # face=2
        ),
        plot.title = 
          element_markdown(
            hjust = 0.5,
            color = "black",
            size = 19)
    )
)
```

```{r Gráfico Ecoli periodo1, warning = FALSE, message = FALSE}
(ecoli_p1 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"1990" &
                                 ano_coleta<="2000"),
                    aes(codigo,
                        escherichia_coli))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3200,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=800,
             ymax=3200,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=160,
             ymax=800,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=160,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Escherichia coli no período 1990-2000",
         x="Estação",
         y="NMP/100mL")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                 max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico Ecoli periodo2, warning = FALSE, message = FALSE}
(ecoli_p2 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"2000" &
                                 ano_coleta<="2010"),
                    aes(codigo,
                        escherichia_coli))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3200,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=800,
             ymax=3200,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=160,
             ymax=800,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=160,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Escherichia coli no período 2000-2010",
         x="Estação",
         y="NMP/100mL")+
    scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                       n.breaks = 9,
                       limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                  max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                       trans = "log10",
                       labels = scales::number_format(accuracy = 1,
                                                      decimal.mark = ",",
                                                      big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico Ecoli periodo3, warning = FALSE, message = FALSE}
(ecoli_p3 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"2010" &
                                 ano_coleta<="2020"),
                    aes(codigo,
                        escherichia_coli))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3200,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=800,
             ymax=3200,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=160,
             ymax=800,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=160,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Escherichia coli no período 2010-2020",
         x="Estação",
         y="NMP/100mL")+
    scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                       n.breaks = 9,
                       limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                  max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                       trans = "log10",
                       labels = scales::number_format(accuracy = 1,
                                                      decimal.mark = ",",
                                                      big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Sumário Ecoli, warning=FALSE, message = FALSE,}
(sum_ecoli_p1 <- plan_wide_19902020 %>%
    select(codigo, escherichia_coli, ano_coleta) %>% 
    filter(ano_coleta>"1990" &
              ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(escherichia_coli, 
           na.rm = TRUE),
     q1 = 
       quantile(escherichia_coli, 0.25, 
                na.rm = TRUE),
     median = 
       median(escherichia_coli, 
              na.rm = TRUE),
     mean = 
       mean(escherichia_coli, 
            na.rm= TRUE),
     q3 = 
       quantile(escherichia_coli, 0.75, 
                na.rm = TRUE),
     max = 
       max(escherichia_coli, 
           na.rm = TRUE))
)

(sum_ecoli_p2 <- plan_wide_19902020 %>%
    select(codigo, escherichia_coli, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(escherichia_coli, 
            na.rm = TRUE),
      q1 = 
        quantile(escherichia_coli, 0.25, 
                 na.rm = TRUE),
      median = 
        median(escherichia_coli, 
               na.rm = TRUE),
      mean = 
        mean(escherichia_coli, 
             na.rm= TRUE),
      q3 = 
        quantile(escherichia_coli, 0.75, 
                 na.rm = TRUE),
      max = 
        max(escherichia_coli, 
            na.rm = TRUE))
)

(sum_ecoli_p3 <- plan_wide_19902020 %>%
    select(codigo, escherichia_coli, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(escherichia_coli, 
            na.rm = TRUE),
      q1 = 
        quantile(escherichia_coli, 0.25, 
                 na.rm = TRUE),
      median = 
        median(escherichia_coli, 
               na.rm = TRUE),
      mean = 
        mean(escherichia_coli, 
             na.rm= TRUE),
      q3 = 
        quantile(escherichia_coli, 0.75, 
                 na.rm = TRUE),
      max = 
        max(escherichia_coli, 
            na.rm = TRUE))
)
```

```{r Salvando ecoli, warning=FALSE, message = FALSE,}
ggsave("ecoli.png",
       plot = ecoli,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p1.png",
       plot = ecoli_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p2.png",
       plot = ecoli_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p3.png",
       plot = ecoli_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Nitrogênio amoniacal

```{r Gráfico Nitrogênio total facetted, fig.cap="nitrogenio-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(namon <- plan_wide_19902020 %>% 
  boxplot_namon(
    eixo_y = nitrogenio_amoniacal,
    titulo = "Nitrogênio Amoaniacal no período 1990-2020"
    )+
  facet_wrap(~periodo)
 )
```

```{r Gráfico Nitrogênio line, warning = FALSE, message = FALSE}
periodo_inicial <- as.Date("1990-01-01", "%Y-%m-%d")
periodo_final <- as.Date("2021-01-01",  "%Y-%m-%d")

(nitro_line <- 
  plan_wide_19902020 %>%
  filter(ano_coleta > "1990" &
           ano_coleta <= "2020") %>%
  dplyr::select(codigo, nitrogenio_amoniacal, data_coleta, periodo) %>%
  # group_by(codigo) %>%
  mutate(
    ponto_monitoramento = case_when(
      codigo == "87398500" ~ "PM1",
      codigo == "87398980" ~ "PM2",
      codigo == "87398900" ~ "PM3",
      codigo == "87398950" ~ "PM4",
      codigo == "87405500" ~ "PM5",
      codigo == "87406900" ~ "PM6",
      codigo == "87409900" ~ "PM7"
    )
  ) %>% 
    # pivot_wider(
    #   names_from = codigo,
    #   values_from = nitro_amon,
    #   id_cols = data_coleta
    # ) %>% 
    ggplot(
      aes(x = data_coleta,
          y = nitrogenio_amoniacal,
          # color = codigo
      ))+
    # geom_rect(
    #   aes(xmin = periodo_inicial, 
    #       xmax = periodo_final,
    #       ymin = 13.3, 
    #       ymax = Inf,
    #       alpha= 0.005,
    #       fill= "#ac5079"),
    # show.legend = FALSE)+ #>pior classe
    # geom_rect(
    #   aes(xmin = periodo_inicial, 
    #       xmax = periodo_final,
  #       ymin= 3.7,
  #       ymax= 13.3,
  #       alpha= 0.005,
  #       fill= "#fcf7ab"),
  #    show.legend = FALSE)+ #classe 3
  # geom_rect(
  #   aes(xmin = periodo_inicial, 
  #       xmax = periodo_final,
  #       ymin= 0,
  #       ymax= 3.7,
  #       alpha= 0.005,
  #       fill= "blue"
  #         # "#8dcdeb"
  #         ),
  #    show.legend = FALSE)+ #classe 1
  annotate("rect",
           xmin= periodo_inicial,
           xmax= periodo_final,
           ymin=13.3,
           ymax=Inf,
           alpha= 0.7,
           fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin= periodo_inicial,
             xmax= periodo_final,
             ymin=3.7,
             ymax=13.3,
             alpha= 0.7,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin= periodo_inicial,
             xmax= periodo_final,
             ymin= -Inf,
             ymax=3.7,
             alpha= 0.7,
             fill="#8dcdeb")+ #classe 1
    geom_line(
      # aes(color = codigo),
      na.rm = TRUE)+
    geom_point(
      # aes(color = codigo),
      na.rm = TRUE)+
    scale_x_date(
      limits = as.Date(c(
        "1990-01-01", 
        "2021-01-01"
        # NA #pode usar NA também
      )),
      expand = c(0.0, 0.0),
      date_breaks = "10 years",
      minor_breaks = "5 years",
      date_labels = "%Y",
    )+
    # geom_smooth(
    #   # aes(color = codigo),
    #   method = "lm",
    #   # formula = y ~ poly(x, 2),
    #   # span = 0.2,
    #   se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
    #   aes(group = 1),
    #   alpha =.5,
    #   na.rm = TRUE,
    #   size = 0.3,
    #   # fullrange = TRUE,
  #   # show.legend = TRUE
  # )+
  # stat_smooth(
  #   geom = "smooth",
  #   # span = 0.2,
  #   se = FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
  #   # aes(group = 1),
  #   # alpha =.5,
  #   na.rm = TRUE,
  #   # size = 0.3,
  #   fullrange = TRUE,
  #   show.legend = TRUE
  # )+
  facet_wrap(
    ~ponto_monitoramento,
    nrow = 4,
  )+
    theme_bw()
)
```

```{r Gráfico Nitrogênio total periodo1, warning = FALSE, message = FALSE}
(namon_p1 <- ggplot(plan_wide_19902020 %>% 
                      filter(ano_coleta>"1990" &
                               ano_coleta<="2000"),
                    aes(codigo,
                        nitrogenio_amoniacal))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3.7,
             ymax=13.3,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=3.7,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 1990-2000",
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE),
                                 max(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Nitrogênio total periodo2, warning = FALSE, message = FALSE}
(namon_p2 <- ggplot(plan_wide_19902020 %>% 
                      filter(ano_coleta>"2000" &
                               ano_coleta<="2010"),
                    aes(codigo,
                        nitrogenio_amoniacal))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 2000-2010",
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE),
                                 max(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Nitrogênio total periodo3, warning = FALSE, message = FALSE}
(namon_p3 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"2010" &
                                 ano_coleta<="2020"),
                    aes(codigo,
                        nitrogenio_amoniacal))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 2010-2020",
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE),
                                 max(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Namon 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3)
```

```{r Sumário Nitrogênio total, warning=FALSE, message = FALSE,}
(sum_namon_p1 <- plan_wide_19902020 %>%
   select(codigo, nitrogenio_amoniacal, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(nitrogenio_amoniacal, 
           na.rm = TRUE),
     q1 = 
       quantile(nitrogenio_amoniacal, 0.25, 
                na.rm = TRUE),
     median = 
       median(nitrogenio_amoniacal, 
              na.rm = TRUE),
     mean = 
       mean(nitrogenio_amoniacal, 
            na.rm= TRUE),
     q3 = 
       quantile(nitrogenio_amoniacal, 0.75, 
                na.rm = TRUE),
     max = 
       max(nitrogenio_amoniacal, 
           na.rm = TRUE),
      n = 
       length(nitrogenio_amoniacal)
   )
)

(sum_namon_p2 <- plan_wide_19902020 %>%
    select(codigo, nitrogenio_amoniacal, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(nitrogenio_amoniacal, 
            na.rm = TRUE),
      q1 = 
        quantile(nitrogenio_amoniacal, 0.25, 
                 na.rm = TRUE),
      median = 
        median(nitrogenio_amoniacal, 
               na.rm = TRUE),
      mean = 
        mean(nitrogenio_amoniacal, 
             na.rm= TRUE),
      q3 = 
        quantile(nitrogenio_amoniacal, 0.75, 
                 na.rm = TRUE),
      max = 
        max(nitrogenio_amoniacal, 
            na.rm = TRUE))
)

(sum_namon_p3 <- plan_wide_19902020 %>%
    select(codigo, nitrogenio_amoniacal, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(nitrogenio_amoniacal, 
            na.rm = TRUE),
      q1 = 
        quantile(nitrogenio_amoniacal, 0.25, 
                 na.rm = TRUE),
      median = 
        median(nitrogenio_amoniacal, 
               na.rm = TRUE),
      mean = 
        mean(nitrogenio_amoniacal, 
             na.rm= TRUE),
      q3 = 
        quantile(nitrogenio_amoniacal, 0.75, 
                 na.rm = TRUE),
      max = 
        max(nitrogenio_amoniacal, 
            na.rm = TRUE))
)
```

```{r Salvando namon, warning=FALSE, message = FALSE,}
ggsave("namon.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = namon,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p1.png",
       plot = namon_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p2.png",
       plot = namon_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p3.png",
       plot = namon_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Turbidez

```{r Gráfico Turbidez facetted, fig.cap="turbidez-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(turb <- ggplot(plan_wide_19902020,
                   aes(codigo,
                       turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "Turbidez no período 1990-2020",
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.05)),
                      n.breaks = 8,
                      limits = c(
                        # 1,
                        min(plan_wide_19902020$turbidez, na.rm = TRUE),
                        # 500
                        max(plan_wide_19902020$turbidez, na.rm = TRUE)
                      ),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Turbidez line, warning = FALSE, message = FALSE}
(turb_line <- plan_wide_19902020 %>%
  filter(ano_coleta > "1990" &
           ano_coleta <= "2020") %>%
  select(codigo, turbidez, data_coleta, periodo) %>%
  group_by(codigo) %>%
  ggplot(
    aes(x = data_coleta,
        y = turbidez,
        color = codigo
    ))+
    geom_line(
      # aes(color = codigo),
      na.rm = TRUE)+
    geom_point(
      # aes(color = codigo),
      na.rm = TRUE)+
    scale_x_date(
      limits = as.Date(c(
        "1990-01-01", 
        "2021-01-01"
        # NA #pode usar NA também
      )),
      expand = c(0.0, 0.0),
      date_breaks = "10 years",
      minor_breaks = "5 years",
      date_labels = "%Y",
    )+
  # geom_smooth(
  #   # aes(color = codigo),
  #   method = "lm",
  #   # formula = y ~ poly(x, 2),
  #   # span = 0.2,
  #   se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
  #   aes(group = 1),
  #   alpha =.5,
  #   na.rm = TRUE,
  #   size = 0.3,
  #   # fullrange = TRUE,
  #   # show.legend = TRUE
  # )+
  # stat_smooth(
  #   geom = "smooth",
  #   # span = 0.2,
  #   se = FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
  #   # aes(group = 1),
  #   # alpha =.5,
  #   na.rm = TRUE,
  #   # size = 0.3,
  #   fullrange = TRUE,
  #   show.legend = TRUE
  # )+
  facet_wrap(
    ~codigo,
    nrow = 4,
  )+
  theme_bw()
)
```

```{r Gráfico Turbidez periodo1, warning = FALSE, message = FALSE}
(turb_p1 <- ggplot(plan_wide_19902020 %>% 
                     filter(ano_coleta>"1990" &
                              ano_coleta<="2000"),
                   aes(codigo,
                       turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 1990-2000",
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Turbidez periodo2, warning = FALSE, message = FALSE}
(turb_p2 <- ggplot(plan_wide_19902020 %>% 
                     filter(ano_coleta>"2000" &
                              ano_coleta<="2010"),
                   aes(codigo,
                       turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 2000-2010",
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Turbidez periodo3, warning = FALSE, message = FALSE}
(turb_p3 <- ggplot(plan_wide_19902020 %>% 
                     filter(ano_coleta>"2010" &
                              ano_coleta<="2020"),
                   aes(codigo,
                       turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 2010-2020",
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico turb 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3)
```

```{r Sumário Turbidez, warning=FALSE, message = FALSE,}
(sum_turb_p1 <- plan_wide_19902020 %>%
   select(codigo, turbidez, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(turbidez, 
           na.rm = TRUE),
     q1 = 
       quantile(turbidez, 0.25, 
                na.rm = TRUE),
     median = 
       median(turbidez, 
              na.rm = TRUE),
     mean = 
       mean(turbidez, 
            na.rm= TRUE),
     q3 = 
       quantile(turbidez, 0.75, 
                na.rm = TRUE),
     max = 
       max(turbidez, 
           na.rm = TRUE))
)

(sum_turb_p2 <- plan_wide_19902020 %>%
    select(codigo, turbidez, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(turbidez, 
               na.rm = TRUE),
      mean = 
        mean(turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(turbidez, 
            na.rm = TRUE))
)

(sum_turb_p3 <- plan_wide_19902020 %>%
    select(codigo, turbidez, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(turbidez, 
               na.rm = TRUE),
      mean = 
        mean(turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(turbidez, 
            na.rm = TRUE))
) 
```

```{r Salvando turb, warning=FALSE, message = FALSE,}
ggsave("turb.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = turb,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p1.png",
       plot = turb_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p2.png",
       plot = turb_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p3.png",
       plot = turb_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### pH

```{r Gráfico pH facetted, fig.cap="pH-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(pH <- ggplot(plan_wide_19902020,
                 aes(codigo,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "pH no período 1990-2020",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH periodo1, warning = FALSE, message = FALSE}
(pH_p1 <- ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta>"1990" &
                            ano_coleta<="2000"),
                 aes(codigo,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 1990-2000",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH periodo2, warning = FALSE, message = FALSE}
(pH_p2 <- ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta>"2000" &
                            ano_coleta<="2010"),
                 aes(codigo,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 2000-2010",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH periodo3, warning = FALSE, message = FALSE}
(pH_p3 <- ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta>"2010" &
                            ano_coleta<="2020"),
                 aes(codigo,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 2010-2020",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3)
```

```{r Sumário pH, warning=FALSE, message = FALSE,}
(sum_pH_p1 <- plan_wide_19902020 %>%
   select(codigo, pH, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(pH, 
           na.rm = TRUE),
     q1 = 
       quantile(pH, 0.25, 
                na.rm = TRUE),
     median = 
       median(pH, 
              na.rm = TRUE),
     mean = 
       mean(pH, 
            na.rm= TRUE),
     q3 = 
       quantile(pH, 0.75, 
                na.rm = TRUE),
     max = 
       max(pH, 
           na.rm = TRUE))
)

(sum_pH_p2 <- plan_wide_19902020 %>%
    select(codigo, pH, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
) 

(sum_pH_p3 <- plan_wide_19902020 %>%
    select(codigo, pH, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
)
```

```{r Salvando pH, warning=FALSE, message = FALSE,}
ggsave("pH.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = pH,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p1.png",
       plot = pH_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p2.png",
       plot = pH_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p3.png",
       plot = pH_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Sólidos totais

```{r Gráfico SólTot facetted, fig.cap="sólidos-totais-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(SolTot <- ggplot(plan_wide_19902020,
                  aes(codigo,
                      solidos_totais))+
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "Sólidos totais no período 1990-2020",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico SólTot periodo1, warning = FALSE, message = FALSE}
(SolTot_p1 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"1990" &
                                ano_coleta<="2000"),
                     aes(codigo,
                         solidos_totais))+
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 1990-2000",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico SólTot periodo2, warning = FALSE, message = FALSE}
(SolTot_p2 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"2000" &
                                ano_coleta<="2010"),
                     aes(codigo,
                         solidos_totais))+
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 2000-2010",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico SólTot periodo3, warning = FALSE, message = FALSE}
(SolTot_p3 <- ggplot(plan_wide_19902020 %>% 
                        filter(ano_coleta>"2010" &
                                  ano_coleta<="2020"),
                     aes(codigo,
                         solidos_totais))+
    annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=500,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Sólidos totais no período 2010-2020",
         x="Estação",
         y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico SólTot 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3)
```

```{r Sumário Sólidos Totais, warning=FALSE, message = FALSE,}
(sum_SolTot_p1 <- plan_wide_19902020 %>%
   select(codigo, solidos_totais, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(solidos_totais, 
           na.rm = TRUE),
     q1 = 
       quantile(solidos_totais, 0.25, 
                na.rm = TRUE),
     median = 
       median(solidos_totais, 
              na.rm = TRUE),
     mean = 
       mean(solidos_totais, 
            na.rm= TRUE),
     q3 = 
       quantile(solidos_totais, 0.75, 
                na.rm = TRUE),
     max = 
       max(solidos_totais, 
           na.rm = TRUE))
)

(sum_SolTot_p2 <- plan_wide_19902020 %>%
    select(codigo, solidos_totais, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(solidos_totais, 
            na.rm = TRUE),
      q1 = 
        quantile(solidos_totais, 0.25, 
                 na.rm = TRUE),
      median = 
        median(solidos_totais, 
               na.rm = TRUE),
      mean = 
        mean(solidos_totais, 
             na.rm= TRUE),
      q3 = 
        quantile(solidos_totais, 0.75, 
                 na.rm = TRUE),
      max = 
        max(solidos_totais, 
            na.rm = TRUE))
)

(sum_SolTot_p3 <- plan_wide_19902020 %>%
    select(codigo, solidos_totais, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(solidos_totais, 
            na.rm = TRUE),
      q1 = 
        quantile(solidos_totais, 0.25, 
                 na.rm = TRUE),
      median = 
        median(solidos_totais, 
               na.rm = TRUE),
      mean = 
        mean(solidos_totais, 
             na.rm= TRUE),
      q3 = 
        quantile(solidos_totais, 0.75, 
                 na.rm = TRUE),
      max = 
        max(solidos_totais, 
            na.rm = TRUE))
)
```

```{r Salvando SolTot, warning=FALSE, message = FALSE,}
ggsave("SolTot.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = SolTot,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p1.png",
       plot = SolTot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p2.png",
       plot = SolTot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p3.png",
       plot = SolTot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### IQA

```{r Gráfico IQA facetted, fig.cap="iqa-gravataí no período 1990-2020", echo = FALSE, message=FALSE, warning=FALSE}
(iqa <-ggplot(plan_wide_19902020,
              aes(codigo,
                  iqa, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=25,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=25,
            ymax=50,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=50,
            ymax=70,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=70,
            ymax=90,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=90,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "Variação do IQA no período 1990-2020",
        x="Estação",
        y="IQA")+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
   # theme(axis.title.y = element_blank())
)
```

```{r Gráfico IQA periodo1, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p1 <-ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta > "1990" &
                            ano_coleta <= "2000"),
                 aes(codigo,
                     iqa, na.rm = TRUE))+
    annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=25,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=25,
            ymax=50,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=50,
            ymax=70,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=70,
            ymax=90,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=90,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7,
                 na.rm = TRUE)+
    labs(title = "Variação do IQA no período 1990-2000",
         x="Estação",
         y="")+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
   theme_grafs()+
   theme(axis.title.y = element_blank())
)
```

```{r Gráfico IQA periodo2, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p2 <-ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta > "2000" &
                            ano_coleta <= "2010"),
                 aes(codigo,
                     iqa, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=25,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=25,
            ymax=50,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=50,
            ymax=70,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=70,
            ymax=90,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=90,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA no período 2000-2010",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()+
   theme(axis.title.y = element_blank()
   )
)
```

```{r Gráfico IQA periodo3, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p3 <-ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta > "2010" &
                            ano_coleta <= "2020"),
                 aes(codigo,
                     iqa, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=25,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=25,
            ymax=50,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=50,
            ymax=70,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=70,
            ymax=90,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=90,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA no período 2010-2020",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
    theme_grafs()+
    theme(axis.title.y = element_blank())
)
```

```{r Gráfico IQA 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3)
```

```{r Sumário IQA, warning=FALSE, message = FALSE,}
(sum_IQA_p1 <- plan_wide_19902020 %>%
   select(codigo, iqa, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(iqa, 
           na.rm = TRUE),
     q1 = 
       quantile(iqa, 0.25, 
                na.rm = TRUE),
     median = 
       median(iqa, 
              na.rm = TRUE),
     mean = 
       mean(iqa, 
            na.rm= TRUE),
     q3 = 
       quantile(iqa, 0.75, 
                na.rm = TRUE),
     max = 
       max(iqa, 
           na.rm = TRUE),
     n = 
        length(iqa)
   )
)

(sum_IQA_p2 <- plan_wide_19902020 %>%
    select(codigo, iqa, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(iqa, 
            na.rm = TRUE),
      q1 = 
        quantile(iqa, 0.25, 
                 na.rm = TRUE),
      median = 
        median(iqa, 
               na.rm = TRUE),
      mean = 
        mean(iqa, 
             na.rm= TRUE),
      q3 = 
        quantile(iqa, 0.75, 
                 na.rm = TRUE),
      max = 
        max(iqa, 
            na.rm = TRUE),
      n = 
        length(iqa)
      )
)

(sum_IQA_p3 <- plan_wide_19902020 %>%
    select(codigo, iqa, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>%
    # ?as_factor(codigo) %>% 
    group_by(codigo) %>%
    summarize(
      min = 
        min(iqa, 
            na.rm = TRUE),
      q1 = 
        quantile(iqa, 0.25, 
                 na.rm = TRUE),
      median = 
        median(iqa, 
               na.rm = TRUE),
      mean = 
        mean(iqa, 
             na.rm= TRUE),
      q3 = 
        quantile(iqa, 0.75, 
                 na.rm = TRUE),
      max = 
        max(iqa, 
            na.rm = TRUE),
      n = 
        length(iqa),
      NAs = 
        sum(is.na(iqa))
      ) %>% 
  mutate(
    "%NA" = NAs/n*100
  )
)

```

```{r Salvando iqa, warning=FALSE, message = FALSE,}
ggsave("iqa.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = iqa,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p1.png",
       plot = iqa_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p2.png",
       plot = iqa_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p3.png",
       plot = iqa_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

## Testando coisas

```{r Testando coisas, include = FALSE, warning=FALSE, message = FALSE,}
# plan_wide_19902020 %>% 
#    select(codigo, oxigenio_dissolvido, ano_coleta) %>% 
#    ggplot(aes(ano_coleta, oxigenio_dissolvido, 
#       col = codigo))+
#    geom_line()+
#    facet_wrap(~ codigo, ncol = 7)

# df111 <- data.frame(x = c(1:100))
# glimpse(df111)
# df111$y <- 2 + 3 * df111$x + rnorm(100, sd = 40)
# 
# lm_eqn <- function(df111){
#     m <- lm(y ~ x, df111);
#     eq <- substitute(y == a + b %.% x*","~~r^2~"="~r2,
#          list(a = format(unname(coef(m)[1]), digits = 2),
#               b = format(unname(coef(m)[2]), digits = 2),
#              r2 = format(summary(m)$r.squared, digits = 3)))
#     as.character(as.expression(eq));
# } 
# p2 <- p111 +
#   geom_text(x = 25, y = 300,
#             label = lm_eqn(df111),
#             parse = TRUE)
# p2
# 
# 
# lm_eqc <- function(plan_wide_19902020){
#    m <- lm(oxigenio_dissolvido ~ codigo, plan_wide_19902020);
#    eq <- substitute(y == a + b %.% x*","~~r^2~"="~r2,
#                     list(a = format(unname(coef(m)[1]), digits = 2),
#                          b = format(unname(coef(m)[2]), digits = 2),
#                          r2 = format(summary(m)$r.squared, digits = 3)))
#    as.character(as.expression(eq));
# }
# 
# (od_p1 <-ggplot(plan_wide_19902020 %>%
#                    filter(ano_coleta>"1990" &
#                              ano_coleta<="2000"),
#                 aes(codigo,
#                     oxigenio_dissolvido))+
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=-Inf,
#                ymax=2,
#                alpha=1,
#                fill="#ac5079")+ #>pior classe
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=2,
#                ymax=4,
#                alpha=1,
#                fill="#eb5661")+ #classe 4
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=4,
#                ymax=5,
#                alpha=1,
#                fill="#fcf7ab")+ #classe 3
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=5,
#                ymax=6,
#                alpha=1,
#                fill="#70c18c")+ #classe 2
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=6,
#                ymax=Inf,
#                alpha=1,
#                fill="#8dcdeb")+ #classe 1
#       stat_boxplot(geom = 'errorbar',
#                    width=0.3,
#                    position = position_dodge(width = 0.65))+
#       geom_boxplot(fill='#F8F8FF',
#                    color="black",
#                    outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
#                    width= 0.7)+
#       labs(title = "Oxigênio Dissolvido no período 1990-2000",
#            x="Estação",
#            y="mg/L")+
#       # geom_jitter(width = .05,
#       #             alpha=.2,
#       #             size=1.5,
#       #             color="black")+
#       scale_y_continuous(expand = expansion(mult = c(0,0)),
#                          n.breaks = 11,
#                          limits = c(-1,21))+
#       scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
#       geom_smooth(method = "lm",
#                   se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
#                   aes(group=1),
#                   alpha=.5,
#                   na.rm = TRUE,
#                   size = 1)+
#       # annotate(geom_text(aes(x = "87405500", y = 15),
#       #                    label = lm_eqc(plan_wide_19902020),
#       #                    parse = TRUE,
#       #                    inherit.aes = TRUE,
#       #                    check_overlap = TRUE))+
#       #  geom_line(
#       #     aes(color="red"),
#       #     alpha=.0,
#       # )+
#       # scale_color_manual("Legenda",
#       #                    guide="legend",
#       #                    values = c("Classe 1"="#8dcdeb",
#       #                               "Classe 2"="#70c18c",
#       #                               "Classe 3"="#fcf7ab",
#       #                               "Classe 4"="#eb5661",
#       #                               "Pior Classe"="#ac5079"))+
#    # guides(color=guide_legend(override.aes = list(linetype=c(1,1,1,1,1),
#    #                                               lwd=c(2,2,2,2,2),
#    #                                               shape=c(NA,NA,NA,NA,NA),
#    #                                               alpha=1)))+
#       theme(legend.position = "bottom")+
#       theme_classic())

# list1111 <- sessionInfo()
# list1111$loadedOnly

# install.packages("ggpmisc")
# library(ggpmisc)

# summary(lm(plan_wide_19902020$codigo~plan_wide_19902020$dbo))
# 
# 
# p <- ggplot(data, aes(y=number, x=pod)) +
#   geom_boxplot()
# print(p)

# install.packages("GGally")


# fit = lm(plan_wide_19902020$oxigenio_dissolvido~ plan_wide_19902020$codigo)
# summary(fit)
# summary.lm(fit)
# 
# pacman::p_load(esquisse)

# sumario <- function(x, y){
#   x %>% 
#     group_by(codigo) %>%
#     summarise(
#       list(min= ~min(y, na.rm = TRUE), 
#            Q1= ~quantile(y, probs = 0.25),
#            median= ~median(y, na.rm = TRUE), 
#            Q3= ~quantile(y, probs = 0.75),
#            max= ~max(y, na.rm = TRUE)),
#       .groups = "drop"
#       )
# }
```

### Correlação

```{r Correlação, fig.cap="correlação-parametros-qualidade-agua-gravataí no período 1990-2020", time_it = TRUE, warning=FALSE, message = FALSE,}
parametros_IQA %>% 
  dplyr::select(
    -codigo,
    -ano_coleta,
    -nitrogenio_total
    ) %>% 
  # group_by(codigo) %>% 
  rename(
    
    CE = condutividade,
    E_coli = escherichia_coli,
    OD = oxigenio_dissolvido,
    ST = solidos_totais,
    Turb = turbidez,
    Temp = temperatura_agua,
    Ptot = fosforo_total,
    # NTot = nitrogenio_total,
    NAmon = nitrogenio_amoniacal,
    NTK = nitrogenio_kjeldahl
  ) %>% 
  ggcorr(
    method = "complete.obs",
    # "pearson",
    # "pairwise",
    name = "Correlação",
    label = TRUE,
    label_alpha = TRUE,
    digits = 3,
    low = "#3B9AB2",
    mid = "#EEEEEE",
    high = "#F21A00",
    # palette = "RdYlBu",
    layout.exp = 0,
    legend.position = "left",
    label_round = 3,
    # legend.size = 18,
    geom = "tile",
    nbreaks = 10,
  )+
  labs(title = "Correlação entre parâmetros físico-químicos na\nBacia Hidrográfica do rio Gravataí no período 1990-2020")+
  theme_linedraw()+
  theme(
    legend.position = c(0.15, 0.6),
    legend.title = element_text(size = 16),
    legend.text = element_text(size = 14),
    # legend.spacing = unit(element_text(),
                          # units = 5)
    plot.title = element_text(hjust = 0.5,
                              size = 16)
  )

# Gráfico das correlações entre todos os parâmetros com significância
correl_IQA <- parametros_IQA %>%
  dplyr::select(-codigo) %>%
  ggpairs(title = "Correlação entre parâmetros que compõem o IQA",
          axisLabels = "show")

correlacao_pIQA <- parametros_IQA %>% 
  group_by(codigo) %>% 
  correlation::correlation()

correlacao_pIQA %>% 
  # glimpse()
  filter(
    p < 0.001
  ) %>% 
  t() %>% 
  summary()

parametros_IQA %>% 
  # group_by(codigo) %>% 
  dplyr::select(
    nitrogenio_kjeldahl, condutividade
  ) %>% 
  # correlation::cor_test() %>% 
  plot()

```

### Condutividade elétrica

```{r Gráfico cond_elet facetted, fig.cap="condutividade-eletrica-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(cond_elet <- ggplot(plan_wide_19902020,
                        aes(codigo,
                            condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
      labs(title = "Condutividade elétrica no período 1990-2020",
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico cond_elet periodo1, warning = FALSE, message = FALSE}
(cond_elet_p1 <- ggplot(plan_wide_19902020 %>% 
                          filter(ano_coleta>"2000" &
                                   ano_coleta<="2010"),
                        aes(codigo,
                            condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
      labs(title = "Condutividade elétrica no período 1990-2000",
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico cond_elet periodo2, warning = FALSE, message = FALSE}
(cond_elet_p2 <- ggplot(plan_wide_19902020 %>% 
                          filter(ano_coleta>"2000" &
                                   ano_coleta<="2010"),
                        aes(codigo,
                            condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 2000-2010",
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico cond_elet periodo3, warning = FALSE, message = FALSE}
(cond_elet_p3 <- ggplot(plan_wide_19902020 %>% 
                          filter(ano_coleta>"2010" &
                                   ano_coleta<="2020"),
                        aes(codigo,
                            condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 2010-2020",
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico cond_elet 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3)
```

```{r Sumário cond_elet, warning=FALSE, message = FALSE}
(sum_cond_elet_p1 <- plan_wide_19902020 %>%
   select(codigo, condutividade, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(condutividade, 
           na.rm = TRUE),
     q1 = 
       quantile(condutividade, 0.25, 
                na.rm = TRUE),
     median = 
       median(condutividade, 
              na.rm = TRUE),
     mean = 
       mean(condutividade, 
            na.rm= TRUE),
     q3 = 
       quantile(condutividade, 0.75, 
                na.rm = TRUE),
     max = 
       max(condutividade, 
           na.rm = TRUE))
)

(sum_cond_elet_p2 <- plan_wide_19902020 %>%
    select(codigo, condutividade, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(condutividade, 
               na.rm = TRUE),
      mean = 
        mean(condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(condutividade, 
            na.rm = TRUE))
)

(sum_cond_elet_p3 <- plan_wide_19902020 %>%
    select(codigo, condutividade, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(condutividade, 
               na.rm = TRUE),
      mean = 
        mean(condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(condutividade, 
            na.rm = TRUE),
      n = 
        length(condutividade))
)

# plan_wide_19902020 %>% 
#    select(codigo, IQA) %>% 
#    group_by(codigo) %>% 
#    summarize(
#       min = 
#          min(IQA, 
#              na.rm = TRUE),
#       q1 = 
#          quantile(IQA, 0.25, 
#                   na.rm = TRUE),
#       median = 
#          median(IQA, 
#                 na.rm = TRUE),
#       mean = 
#          mean(IQA, 
#               na.rm= TRUE),
#       q3 = 
#          quantile(IQA, 0.75, 
#                   na.rm = TRUE),
#       max = 
#          max(IQA, 
#              na.rm = TRUE))
```

```{r Salvando cond_elet, warning=FALSE, message = FALSE}
ggsave("cond_elet.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = cond_elet,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p1.png",
       plot = cond_elet_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p2.png",
       plot = cond_elet_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p3.png",
       plot = cond_elet_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")

```

## Textando o texto

-   § falar do comportamento geral dos dados
-   2º § - xº § -\> abordar os principais parâmetros que estão sendo impactados, detalhando, nas estações mais relevantes, como ficaram os quartis/mediana etc.

`r sum_od_p1$PM1[1]`

Os resultados apontam que para o parâmetro OD


## Gráficos exemplos boxplot
```{r elementos do boxplot}
set.seed(2023)
exemplo_boxplot_df <- data.frame(
  PM = c("PM1"),
  # letras = letters[seq( from = 1, to = 1 )],
  Stat1 = rnorm(100, 
                mean = 5, 
                sd = 1.8)
)

(sumario_exemplo_bp <- exemplo_boxplot_df %>% 
    group_by(PM) %>% 
    summarize(
      max = max(Stat1),
      p95 = quantile(Stat1, 0.95),
      p80 = quantile(Stat1, 0.8),
      median = median(Stat1),
      p20 = quantile(Stat1, 0.2),
      p05 = quantile(Stat1, 0.05),
      min = min(Stat1),
    ) %>% 
    t() %>% 
    row_to_names(row_number = 1) %>% 
    as.numeric()
)


(boxplot_example <- exemplo_boxplot_df %>% 
    ggplot(
      aes(
        x = PM,
        y = Stat1,
      )
    )+
    stat_summary(
      fun.data = f,
      geom = 'errorbar',
      width = 0.15,
      position = position_dodge(width = 0.65),
    )+
    stat_summary(
      fun.data = f,
      geom = "boxplot",
      width = 0.40,
      fill = '#F8F8FF',
      color = "black",
      outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
    )+
    labs(
      title = "Elementos do *boxplot*",
      x= NULL,
      y= NULL
    )+
    ggbeeswarm::geom_quasirandom(
      size = 1.4,
      alpha = .3,
      width = .07,
    )+
    scale_y_continuous(
      expand = expansion(mult = c(0,0)),
      n.breaks = 8,
      limits = c(-0.3,12)
    )+
    annotate(
      geom = "text",
      x = 1.55,
      hjust = "right",
      y = sumario_exemplo_bp,
      label = c("Valor máximo", "P95", "P80", "Mediana", "P20", "P05", "Valor mínimo"),
      # fontface = 3
    )+
    geom_richtext(
      x = 0.56,
      y = 9.103998,
      label.color = NA,
      hjust = "center",
      label = "<i>Outliers</i>"
    )+
    geom_curve(
      aes(
        x = 0.6, xend = 0.98,
        y = 9.103998 , yend = 9.103998 , #Outliers
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    #definindo o [
    geom_curve(
      aes(
        x = 0.74, xend = 0.78,
        y = 6.683828, yend = 6.683828,
      ),
      curvature = 0,
      size = 1.0,
      lineend = "butt"
    )+
    geom_curve(
      aes(
        x = 0.74, xend = 0.74,
        y = 3.545771, yend = 6.683828,
      ),
      curvature = 0,
      size = 1.0,
      lineend = "butt"
    )+
    annotate(
      geom = "text",
      x = 0.56,
      hjust = "center",
      y = 5.11,
      label = "Amplitude\n(P80-P20)"
    )+
    geom_curve(
      aes(
        x = 0.74, xend = 0.78,
        y = 3.545771 , yend = 3.545771 ,
      ),
      curvature = 0,
      size = 1.0,
      lineend = "butt"
    )+
    # fim do [
    geom_curve(
      aes(
        x = 0.6, xend = 0.90,
        y = 7.866927 , yend = 7.866927 , #whisker superior
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    annotate(
      geom = "text",
      x = 0.56,
      hjust = "center",
      y = 7.866927,
      label = "Whisker\nsuperior"
    )+
    geom_curve(
      aes(
        x = 0.6, xend = 0.90,
        y = 2.277104  , yend = 2.277104  , #whisker inferior
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    annotate(
      geom = "text",
      x = 0.56,
      hjust = "center",
      y = 2.277104,
      label = "Whisker\ninferior"
    )+
    geom_curve(
      aes(
        x = 1.4, xend = 1.01,
        y = 9.92343, yend = 9.92343, #valor máximo
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.11,
        y = 7.866927 , yend = 7.866927 , #P95
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.22,
        y = 6.683828  , yend = 6.683828  , #P80
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.22,
        y = 4.886935   , yend = 4.886935   , #P50
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.22,
        y = 3.545771, yend = 3.545771, #P20
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.11,
        y = 2.277104, yend = 2.277104, #P05
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.4, xend = 1.01,
        y = 1.282177, yend = 1.282177, #valor mínimo
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    # theme_grafs()+
    theme_bw()+
    theme(
      plot.title = 
        element_markdown(
          hjust = 0.5,
          color = "black",
          size = 19),
    )
)
```

```{r salvando graf elementos boxplot}
ggsave(
  filename = "exemplo_boxplot.png",
  plot = boxplot_example,
  units = c("px"),
  width = (4500)/1.5,
  height = (2993)/1.5,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)
```


```{r criando exemplo tipos boxplot tukey x garrett}
set.seed(2021)

data <- tibble(
  grupo = factor(
    c(rep(
      "Grupo 1", 100), 
      rep("Grupo 2", 250), 
      rep("Grupo 3", 25)
    )
  ),
  valor = c(seq(0, 20, length.out = 100),
            c(rep(0, 5), 
              rnorm(30, 2, .1), 
              rnorm(90, 5.4, .1), 
              rnorm(90, 14.6, .1), 
              rnorm(30, 18, .1), 
              rep(20, 5)
            ),
            rep(seq(0, 20, length.out = 5), 5))
) %>% 
  rowwise() %>%
  mutate(
    valor = if_else(
      grupo == "Grupo 2", valor + rnorm(1, 0, .4), 
      valor
      )
    )

## function to return median and labels
n_fun <- function(x){
  return(
    data.frame(
      y = median(x) - 1.25, 
      label = paste0(
        "n = ",length(x)
      )
    )
  )
}
```

```{r tukey boxplot}
(tukey_n_boxplot <- ggplot(data, 
                           aes(x = grupo, 
                               y = valor)
)+
  stat_boxplot(geom = 'errorbar',
               width = 0.15,
               position = position_dodge(width = 0.65))+
  geom_boxplot(fill = "grey92",
               width = 0.40,
               position = position_dodge(width = 0.65))+
  ## use summary function to add text labels
  stat_summary(
    geom = "text",
    fun.data = n_fun,
    # family = "Oswald",
    size = 5
  )+
  labs(
    title = "Tukey *boxplot*",
    x= NULL,
    # y="mg/L"
  )+
  # theme_grafs()+
  theme_bw()+
  theme(
    axis.text.y = element_text(
      angle = 90, 
      # size=15,
      # face=2
    ),
    plot.title = 
      element_markdown(
        hjust = 0.5,
        color = "black",
        size = 19)
  )
)


(tukey_boxplot <- ggplot(data, aes(x = grupo, 
                                   y = valor)) +
  stat_boxplot(geom = 'errorbar',
               width = 0.15,
               position = position_dodge(width = 0.65))+
  geom_boxplot(fill = "grey92",
               width = 0.40,
               position = position_dodge(width = 0.65)) +
  ## use either geom_point() or geom_jitter()
  geom_point(
    ## draw bigger points
    size = 2,
    ## add some transparency
    alpha = .25,
    ## add some jittering
    position = position_jitter(
      ## control randomness and range of jitter
      seed = 1, width = .2
    )
  )+
  theme_bw()+
  labs(
      title = "Tukey *boxplot*",
      x= NULL,
      # y="mg/L"
    )+
  # theme_grafs()+
  theme_bw()+
  theme(
        axis.text.y = element_text(
          angle = 90, 
          # size=15,
          # face=2
        ),
        plot.title = 
          element_markdown(
            hjust = 0.5,
            color = "black",
            size = 19)
    ))
```

```{r garrett boxplot}
data %>% 
  group_by(grupo) %>% 
  summarize(
    min = min(valor),
    P20 = quantile(valor, 0.20),
    q1 = quantile(valor, 0.25),
    mediana = median(valor),
    q3 = quantile(valor, 0.75),
    P80 = quantile(valor, 0.80),
    max = max(valor)
  ) %>% 
  t() %>% 
  row_to_names(row_number = 1)
  
  
(box_percentile_plot <- ggplot(data, 
       aes(x = grupo, y = valor)
       ) +
      stat_summary(
        fun.data = f,
        geom = 'errorbar',
        width = 0.15,
        position = position_dodge(width = 0.65),
      )+
      stat_summary(
        fun.data = f,
        geom = "boxplot",
        width = 0.40,
        fill = 'grey92',
        color = "black",
        outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
      )+
  # geom_boxplot(fill = "grey92") +
  ## use either geom_point() or geom_jitter()
  geom_point(
    ## draw bigger points
    size = 2,
    ## add some transparency
    alpha = .25,
    ## add some jittering
    position = position_jitter(
      ## control randomness and range of jitter
      seed = 1, width = .2
    )
  )+
  labs(
      title = "*Box Percentile-Plot*",
      x= NULL,
      # y="mg/L"
    )+
  # theme_grafs()+
  theme_bw()+
  theme(
        axis.text.y = element_text(
          angle = 90, 
          # size = 15,
          # face = 2
        ),
        plot.title = 
          element_markdown(
            hjust = 0.5,
            color = "black",
            size = 19)
    )
  )
grid.arrange(
  tukey_boxplot, box_percentile_plot, 
  ncol = 2
  )
fig_tukey_garrett <- plot_grid(tukey_boxplot, box_percentile_plot, 
                               labels = "AUTO")
```

```{r salvando graf exemplo boxplot}
ggsave(
  filename = "tukey_n_boxplot.png",
  plot = tukey_n_boxplot,
  units = c("px"),
  width = 4500,
  height = 2993,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)

ggsave(
  filename = "tukey_boxplot.png",
  plot = tukey_boxplot,
  units = c("px"),
  width = 4500,
  height = 2993,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)

ggsave(
  filename = "box_percentile_plot.png",
  plot = box_percentile_plot,
  units = c("px"),
  width = 4500,
  height = 2993,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)

ggsave(
  filename = "fig_tukey_garrett.png",
  plot = fig_tukey_garrett,
  units = c("px"),
  width = 4500,
  height = 2993,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)
```
